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Small rural agricultural producer's household demographic composition and particitpation in the market economy as factors influrencing cognitive valuation and usage of natural resources in Ancash, Peru

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Title:
Small rural agricultural producer's household demographic composition and particitpation in the market economy as factors influrencing cognitive valuation and usage of natural resources in Ancash, Peru
Creator:
Rocha, Jorge M., 1969-
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English
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vii, 188 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Agricultural land ( jstor )
Agriculture ( jstor )
Amplification ( jstor )
Anthropology ( jstor )
Cognitive models ( jstor )
Cultural anthropology ( jstor )
Demography ( jstor )
Ecology ( jstor )
Economics ( jstor )
Peasant class ( jstor )
Anthropology thesis, Ph.D ( lcsh )
Capitalism -- Peru -- Ancash ( lcsh )
Dissertations, Academic -- Anthropology -- UF ( lcsh )
Farms, Small -- Peru -- Ancash ( lcsh )
Households -- Peru -- Ancash ( lcsh )
Natural resources -- Peru -- Ancash ( lcsh )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 2000.
Bibliography:
Includes bibliographical references (leaves 172-186).
Additional Physical Form:
Also available online.
General Note:
Printout.
General Note:
Vita.
Statement of Responsibility:
by Jorge M. Rocha.

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University of Florida
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All applicable rights reserved by the source institution and holding location.
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0023006756 ( ALEPH )
45080506 ( OCLC )

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SMALL RURAL AGRICULTURAL PRODUCER'S HOUSEHOLD DEMOGRAPHIC COMPOSITION AND PARTICIPATION IN THE MARKET ECONOMY AS FACTORS INFLUENCING COGNITIVE VALUATION AND USAGE OF
NATURAL RESOURCES IN ANCASH, PERU.












By


JORGE M. ROCHA

















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2000
















ACKNOLWEDGMENT S





I wish to thank the faculty and staff of the

Department of Anthropology for all the support and help they provided during the course of my graduate program. Special thanks go to the members of my committee for offering guidance and insights throughout my studies and research.

I would also like to extend my gratitude to the

National Science Foundation for financially supporting the research reported here (Grant 9807155). For also providing financial support during the course of my graduate work, I would like to thank the Universidad de Monterrey, especially Roberto Rebolloso, who has always backed me up unconditionally.

Additional thanks go to all the students and friends at the University of Florida who, in one way or another helped, inspired or supported me during my graduate training, especially to Celina, my wife and friend, who has been there when I have needed her.



ii









Finally, I would like to thank all the people in Peru who made my stay and research there possible, especially the community members at Tumpa who had the time and patience to answer all of my questions, and were willing to show me how they live. It is to them that the work on this dissertation owes the most.

















TABLE OF CONTENTS


ACKNOWLEDGMENTS .....................................

ABSTRACT ............................................ vi

CHAPTERS

1 INTRODUCTION ................................. 1

2 INTENSIFICATION THEORY ....................... 10

3 COGNITIVE SCHEMES AND NATURAL RESOURCES ...... 24

Introduction ................................. 24
Cognition in Social Research ................. 26
Cognition and Anthropology ................... 38
Cognitive Schemas and Resources .............. 42

4 GENERAL METHODOLOGICAL CONSIDERATIONS ........ 45

Research Objectives .......................... 45
Data Gathering Procedures; Scope and
Limitations ............................... 48
Cognitive Schemas of Resources Data .......... 54

5 UNDERSTANDING ENERGY CONCEPTS
AND CALCULATIONS .......................... 60

6 ENERGY CALCULATION OF PEASANT
HOUSEHOLD FLOWS ........................... 67

The Theoretical Objective .................... 67
The Methodology .............................. 70

7 ETHNOGRAPHIC CONTEXT ......................... 84

Tumpa; A Peasant Community ................... 84
Regional Geographical Context ................ 85
Life in Tumpa; Seeking Development ........... 90


iv











8 RESOURCE INTENSITY USAGE, HOUSEHOLD
DEMOGRAPHY AND MARKET PARTICIPATION..........105

Description of Households....................... 105
Demographic Density............................. 111
Market Participation............................ 118

9 MARKET PARTICIPATION AND LOSS OF CULTURAL
KNOWLEDGE..................................... 136

Introduction..................................... 136
Traditional Knowledge vs. Cultural
Knowledge..................................... 138
Loss of Traditional Knowledge.................. 143
The Case of Fertilizers......................... 149
Traditional Ethnoecological Knowledge and
Ecological Sustainability................... 154

10 CONCLUSIONS....................................... 160


APPENDIX EVALUATION OF THE SOLAR TRANSFORMITY
OF GLACIAL RUNOFF ON THE CORDILLERA
BLANCA IN THE PERUVIAN ANDES................. 164

REFERENCES.............................................. 172

BIOGRAPHICAL SKETCH..................................... 187
























V
















Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SMALL RURAL AGRICULTURAL PRODUCER'S HOUSEHOLD DEMOGRAPHIC COMPOSITION AND PARTICIPATION IN THE MARKET ECONOMY AS
FACTORS INFLUENCING COGNITIVE VALUATION AND USAGE OF
NATURAL RESOURCES IN ANCASH, PERU By

Jorge M. Rocha

May 2000


Chairman: Anthony Oliver-Smith Major Department: Anthropology


Employing an energy flows approach to the

understanding of social and cultural phenomena the research in this dissertation suggests that a household's demographic density is a more important factor than market participation in the triggering of agricultural intensification. The research also supports the idea that traditional ethnoecological knowledge is gradually lost as householders depend less on their natural resources and more on economic ones.

To reach these conclusions, a novel combination of

existing methodological techniques from several disciplines


vi









was employed. The conceptual framework of such an approach was also developed and discussed.



















































vii
















CHAPTER 1
INTRODUCTION





Since quite early in my initial contacts with

anthropology, a little more than ten years ago, I developed an interest for the broad topics that are addressed in this dissertation. I delved into anthropology via L6vi-Strauss' (1966) structuralist approach to what I would now call cognitive schemes. I was interested and intrigued by peoples' religious ideologies. Reading on those topics with no real plan in mind, in the sense of doing it systematically, I inevitably came across Roy Rappaport's (1984) classic ethnography, Pigs for the Ancestors. I immediately realized that the structuralist models I was familiar with were somewhat at odds with the way in which he approached the topic. Furthermore, I came to believe that the mode of explanation Rappaport offered with regards to what he called cognized models made more logical sense than those offered by structuralist approaches. It was then that I first seriously considered formally studying anthropology.


1






2


I was not sure of what I wanted to do as an

anthropologist, but knew that I wanted to see if the relationship that Rappaport's work suggested existed between ecosystems and peoples' general mental schemes really did exist. The structuralist in me did not, however, simply disappear. L6vi-Strauss' basic insight that peoples' ways of thinking respond to a simple internal group-shared logic seemed then, as it does now, very accurate. However, after reading some of the critiques of his work, I knew his theory could not account for the changes that might occur in this group-shared logic over time in any credible way. His suggestion that they change because this same logic had an internal dynamic of its own that made it transform itself just did not make logical sense to a young empiricist. There had to be, I came to believe, some kind of external "force" making them change in the socially patterned ways in which they are found by researchers. The notion of a transformative logic, internal to the cognitive schema, that responded to its own binary dynamic left too many things in the dark. It did not, for example, consider how social interactions between individuals influence the process of cognitive schema configurations. I saw in Rappaport's work, and in others who had a similar approach to ideological change (e.g.,






3


Harris 1987), the possibility of studying just what these non-ideoiogical "forces" might be.

The question thus became more a conceptual and

methodological challenge. How could I determine what these "external forces" producing ideological changes were? And furthermore, How would I measure them? That was my position when I began graduate studies. I had no idea as to where I wanted to do my research, nor did I know what type of population I wanted to study. During my first semester as a graduate student I had the luck of taking Dr. Anthony Oliver-Smith's "Rural Peoples in the Modern World" class. I immediately knew peasant populations would make a good case for studying ideological change. After all, one of their defining characteristics is their somewhat ambiguous position on the classic typology of sociocultural change as developed by Elman Service (1975). They, one could say, appear to be in a state of flux. Exposed to the global system of economic market exchange, and "reluctant" to fully incorporate themselves into this process.

Peasants would thus be the peoples I would study. Now the question was where would I study them? Since it was Dr. Oliver-Smith's class which introduced me to peasants, it would be his past research in Peru that would introduce






4


me to the Andes. What I now needed was to develop a strategy for research. One that would allow me to address the issue of cognitive change. Since to study change one needs to have a temporal background as a basis to make comparisons, and because I did not have one (nor was willing to be ABD until I collected the data to have it), I opted for employing intra-cultural variability as an alternative strategy. What I needed to do was to find some set of processes to which a single population was either differentially exposed or involved in, and hypothesize that the differences I might find in the cognitive schemes of whatever my unit of analysis might be would be the result of their degree of exposure to or involvement in these processes. This was precisely what I did.

On reviewing the vast literature on the peasantry I found two very interesting and promising topics that recurred through out: Agricultural intensification and loss of traditional knowledge. They seemed promising because they offered a natural experiment scenario for testing the idea that differences between the ways in which people use their ecosystems are accompanied by differences in their cognitive schemes, just as Rappaport's insights suggested it should.









After a little more than ten years of tinkering with the idea, I finally had what I regarded as a good case for answering my question. The research reported in this dissertation is the result of this effort.

The field work was conducted in a peasant community in the Andean valley known as the Callej6n de Huaylas, in rural Peru. The community is some 60km north of the departmental capital, Huaraz, which is itself some 300km north of Lima. Tumpa, as the community is known, has a population of some 3,000 individuals who make their living from a combination of different economic strategies. In chapter six in I offer a brief schematic description of the general environmental and social characteristics of the field work site. It serves as an historical and ethnographic background for the better understanding of the more detailed processes analyzed throughout the rest of the dissertation.

on chapters one and two I address the conceptual

issues regarding both agricultural intensification (chapter one) and cognitive schemes (chapter two). In chapter one I argue why it is theoretically reasonable to postulate resource use intensity as an important factor that, along with a series of other correlated processes, might help to understand the differences that are found between






6


households in the same community. Then on chapter two I argue why it is also reasonable to regard cognitive processes as probabilistically determined by non-cognitive phenomena such as intensity of resource use as well as general economic orientation.

While reading these chapters I hope it will be clear

to the reader that I do not maintain what might be referred to as a mechanistic view on the idea of infrastructure determinism. In so far as I argue that it is individuals who, living in household units, and in active interactions with their environment as well as with other individuals both within their own household and in others I regard the phenomena as multi-causai. Not only that; I fully recognize that the group held ideas that are studied here are meaningful and open to multiple interpretations by the individuals of the community. However, since the objective of my work was not to analyze the ways in which these 11mental maps" are viewed or interpreted by different members of the community, I did not systematically collect information on those issues. Rather, my objective was to see if I could account for a fraction of the observed differences between householders' "mental maps" in terms of their primary modes of structuring and integrating their






7


environmental and economic strategies in order to meet their varied needs.

Chapters three, four and five present the way in which the research problem was addressed and operationalized. Chapter three offers a general description of how research was conducted. It also describes how data on cognitive schemes was collected and coded so that it could be statistically processed and analyzed. Chapter four is intended as a brief introduction, focused mostly on the methodological issues, to the strategy for ecosystem analysis developed by Howard T. Odum (1994; 1996). In that chapter too, I argue why the use of this particular perspective is adequate for my research purposes. This is so, basically, because in contrast to other similarly inspired perspectives (e.g. Adams 1988), it allows for the systematic measurement of the processes analyzed.

Chapter five constitutes the main methodological

effort of my research. It describes how an energetic model for the analysis of peasant household resource flows was developed, as well as how the data for it was collected, coded and processed for further statistical analysis. If my research has some ideas to offer in terms of methodology, I believe these are outlined in this chapter. Richard Adams suggested some years ago (see Adams 1988:7)






8


that one of the remaining challenges of anthropological research employing energy, or some correlate, as a unit of measurement for phenomena theoretically believed to be of causal importance for the understanding of social processes was precisely in terms of how to operationalize and systematically measure these phenomena. I here offer one possibility.

Chapters seven and eight offer the results and

discussion of my research. In chapter seven the results of analyzing intensity of resource use as dependent on manly, but not solely, household demographic characteristics are given. It is there concluded that for households in the community of Tumpa demographic density is a more important factor than degree of market involvement--even if it is recognized that both are interwoven--for understanding the level of agricultural intensity of individual households.

Finally in chapter eight the results of analyzing how the observed differences in household member's cognitive models are related to the ways in which they structure their interactions both with their ecological and social environments. The central conclusion reached there supports the idea first attracted me into anthropology. That differences in the modes of interacting with ecosystemic processes are very likely accompanied by






9


differences in people's cognitive understandings of these same environmental processes.
















CHAPTER 2
INTENSIFICATION THEORY



There is a long history of research and theorization

within economic anthropology regarding how the expansion of capitalist agriculture, and the subsequent market participation by small householders affects their economic, social, and political relations to the society at large (e.g. Cancian 1991; Hamaza 1987; Stanford 1991) These discussions have also addressed the issue of whether this process will result in their eventual disappearance as subsistence producers. The debate around this fundamentally political question for Latin A~merican rural development efforts has come to known as the "Campesinista Decampesinista" debate (see Feder 1981) However, until recently little research had been done to understand the ecological, as opposed to the politico-economic, dimensions of the problem.

By focusing on the agro-ecological dimensions of peasant lifeways the pioneering research and conceptualization of Netting (1974) sought not only a new way of understanding rural householders, but also an alternative set of



10






11


explanations regarding their characteristic modes of economic and political articulation with the rest of society. In a way, it was hoped that this alternative strategy of conceptualization would yield new insights into the Campesinista-Decampesinista debate, while at the same time offering a better understanding of several other important issues regarding peasant historical transformations. One of these central issues is the process of agricultural intensification. That is, the process by means of which small rural agricultural producers increase the productivity of their plots by making more intensive use of their labor and other social and natural resources.

The theoretical ground work for an understanding of agricultural intensification was set up by Ester Boserup (1965) when she argued that rural producers intensified their agricultural activities mainly as a response to local population changes. At the time Boserup was publishing these first ideas, however, several agricultural development economists were also offering an alternative perspective as to what drove rural householders to intensify their production (for historical details see Staatz and Eicher 1990). The focus of many of these economic models was in part motivated by the huge economic and political importance of rural populations in most third world countries at the






12


time. Under these alternative set of explanations agricultural intensification was not seen as demography driven, but rather as market driven. It was hypothesized that rural producers would intensify their production as a response to clear market signals that allowed them to plan their activities in advance, and seek monetary gains from such planning.

These two competing sets of arguments, each derived from different theoretical perspectives, nonetheless seek to address the same complex phenomena. one can refer to each as the "Demographic Intensification Argument" and the "Market Intensification Argument." (Cf. Kates, et al. 1993). The demographic intensification model as originally developed and refined by Boserup (1990) has been adopted and further developed from an anthropological perspective by Netting (1974; 1993).

Figure 2-1 shows the economic logic behind the model of demographic intensification as it would apply to a community of rural producers (for a similar discussion, but from the perspective of historical demography see Swedlund 1978). The idea is relatively simple; it assumes that an increase in population pressure is actually very similar to an increased demand for foodstuffs, which is represented in the graph as a






13


shift from D1 to D2 given S1 as supply before intensification.



Before intensification After intensification



S2
-BT- R- T

QR- /- S' R I







Q2


Figure 2-1. Demographic Hypothesis



The reason for intensification (shift from S1 to S2) according to this model is that without it the social reproductive capacity of the community is jeopardized. Under increased population pressure community members are confronted with the choice of either lowering their culturally accepted and socially established living standards (shown as SRL = social reproductive limit) or intensifying their production to increase supply and maintain a relatively similar lifestyle. It is a case where Romer's Rule of evolving systems is applicable (Romer 1960). This evolutionary generalization suggests that most changes in






14


evolutionary systems occur in order to allow the system to remain the same; that the small changes introduced by means of natural or cultural selection in one dimension or trait of the system seek to guaranty the continued stability under the same general ecological niche with the same survival strategy, but that these small changes might eventually lead to mayor new adaptive strategies.

In summary, the model's underlying hypothesis is that intensification results as a consequence of the interaction between two variables: increased local demographic pressure and the desire--not necessarily a conscious one, which at times might be a need, to continue having a determined lifestyle.

The hypothesis of the market intensification model, on

the other hand, is completely different, even if the logic is similar. This model has been developed mostly under the influence of neoclassical economic theory as a need to understand the nature of the responses by small householders to Green Revolution agricultural packages (e.g. Goldman and Smith 1994; Hayami 1990). Timmer et al. (1983:78) phrase the hypothesis as follows:


Farm households base their consumption and production
decisions on farm input prices, cash and food crop
output prices, the prices of consumer goods from the
market, the opportunity cost of their members' time






15


either in outside labor markets or on farm production
(including household work), and demand for leisure.


In other words, householders are viewed as making decisions within a completely economic environment determined by the monetary values of the resources employed.

Concentrating only on the production decisions as

determined by food output prices, and all other things being held equal, Figure 2-2 shows the logic behind the intensification of agricultural production as driven by market stimuli.


S1




Pi ------- a S2

V P
............


D

Q1 Q*



Figure 2-2. Market Hypothesis



The reason for intensification, the model suggests, occurs because under S1 there is an unrealized producer surplus (represented by area PlaeP*) since the marginal value of the good in question (i.e., consumers' willingness






16


to pay at the margin) is actually lower. In other words intensification occurs, because there is, for some producers, a profit to be made. Without intensification the cost of this unrealized producer surplus is being absorbed by consumers as a higher price than what they would otherwise be willing to pay. Under competitive equilibrium this unrealized producer surplus would be a consumer surplus according to marginalist value theory.

The shaded area in Figure 2-2 represents the total welfare loss to the society at large under S1 (which is actually being paid by someone somewhere; economists have argued that many times it is paid by government in the form of subsidies to input production prices, or other macro economic prices in other productive sectors). After intensification (shift in the supply curve to the right, or from S1 to S2) the price that consumers have to pay is reduced form P1 to P* resulting in a higher benefit to them; that is, until an equilibrium is reached resulting in an increase in production equal to delta Q* Q1.

Those producers who intensified also fare better, at

times at the expense of those who did not, because they too make a profit. In summary, the market intensification model's underlying hypothesis is that the increase in production is a consequence of the interplay between input






17


and output prices that signal producers to increase their output.

Parallel to this market-driven model of agricultural intensification is the idea that technological innovation plays a major and decisive role in achieving a successful transformation of the productive practices of small householders (e.g. Bonnen 1990; Cotlear 1989; Ruttan and Hayami 1990). However, this line of reasoning has led many economists to worry about the inappropriate allocation of resources and other productive efforts which might be the consequence of macro economic policies that aim at creating a favorable environment for investment in capital intensive forms of agriculture in regions where these conditions do not really exist; i.e., where labor supply is abundant (Timmer, et al. 1983:138).

"Boserup's model of intensification was revolutionary," as Netting (1993:270) correctly points out, "in part because it cast doubt on evolutionary scenarios ... that made technology the primary, indeed the sole, engine of agricultural change." He further points out that intensive agricultural practices are based on skill as opposed to scale (Netting 1993:49), and that local indigenous knowledge must actually be considered as part of the technology available to small householders' productive strategies. Accordingly, he






18


argues, intensification occurs by "increasing the amount of labor applied per unit of land in the total farming operation" (Netting 1993:108), and not by increasing the amount of technological capital per unit of land. Furthermore, as Harris (1977) has argued regarding the role of technology during the process of agricultural intensification, one has to consider technology as interacting with the specific environment where its being used and not simply assume it is there and that it developed from no where. He suggests that one "cannot really talk about technology in an abstract manner" (Harris 1990:120), but rather has to consider it as part of the adaptive strategy of a population in terms of how the diminishing returns on labor per unit of land might trigger the development or adoption of a new technological tool kit.

The crux of the problem appears to be that while the market system is indeed a very effective mechanism for assuring economic efficiency by signaling how individuals should allocate and structure their productive efforts and make use of their natural, social and technological resources in order to arrive at an optimal productive output, it does not appear to be effective concerning the value that should be assigned to resources if ecological sustainable patterns of resource use are to be achieved (Daly 1991; 1993; de Groot






19


1994; Ehrlich 1994) As a consequence of this lack of efficiency regarding "value allocation" it is believed that the cognitive value schemas employed by rural householders to cognize their natural resources will be out of tune with the objective availability of resources--something that might lead to improper use of natural resources.

This limitation of the market system is especially problematic, not only because there seems to be a link between poverty and ecological degradation (Blaikie 1935; World Resources Institute 1992:30), but also because intensification--regardless of whether it is market or demography driven--is a practical necessity for many developing countries that need to alleviate the chronic problems regarding lack of foodstuffs for large sectors of their increasingly urban populations (Ruttan 1990) Thus, even if environmental economists argue that "sustainable growth is an impossibility" (Costanza, et al. 1991:7), the practical dilemma of offering a single set of policy recommendations that balances the need to intensify food production and environmental sustainability continues to elude analysts.

Small Householders, which in most developing countries represent the poorest sectors of the population, when responding to market signals, and because of "the imbalance






20


between their consumption needs ... and their capacity to fulfill them with sustainable agricultural practices" (Garcia-Barrios and Garcia-Barrios 1990:1569), frequently respond to market signals by shortening their traditional fallow cycles, abandoning of manuring practices to restore soil fertility, and by using non-land species thus reducing local gene-pool diversity, all in order to make a profit. Susan Stonich (1992:155), for example, drawing on data from her research in Honduras, concluded that


Small scale, resource-poor farmers often directly cause
environmental deterioration because they are forced to
do so by existing social relationships [between resource competing social classes]. They are under such pressure
to secure a livelihood that the short-term costs of
conservation efforts are prohibitive.


The underlying idea behind this argument is that the social differentiation that results with the penetration of social relations of production aiming towards capital accumulation, and the resulting increased participation in the market economy of rural householders, erodes traditional institutions, and the values therein embodied, resulting in a loss of the traditional ecological rationality and other socially embedded "safety first" mechanisms. In more specific terms, the argument implies that the unequal distribution of land resources between large capitalist






21


agricultural enterprises (generally producing for export, and which receive more support form governmental development programs) and small householders, forces the latter to intensify production in ecologically unsustainable ways so that their livelihoods (social reproduction limits in Figure 2-1) can be maintained in the short run.

In general terms I do not view these two theoretical models, the demographic intensification one or the market intensification one, as being mutually contradictory. Quite the contrary, I argue that they are complementary and that both have to be used by developing a conceptual schema that allows for their integration. In last analysis, I believe the issue to be really a methodological one, for as Swedlund (1978:145) wrote some years ago, "the argument usually involves the question of whether population is the dependent or independent variable." Some could argue that this is not the case, and that the central issue concerns the individual's relationship with society. In a way, this is true, but seen under such perspective the matter remains a purely theoretical issue and no possibility of empirical operationalization is considered. What is needed is a way in which the effects of both demographic and economic factors are considered as causal factors in the process of agricultural intensification, rather than yet another series






22


of debates regarding the nature of individual strategies for mediating articulations with the rest of the social world.

Working within the model of Agrarian Ecology as

developed by Netting (1974; 1976; 1993), Wilk (1984; 1993a; 1993b), and others, I view households as an appropriate unit of analysis that allows for the conceptual integration of both models and an empirical assessment of how the variables employed in each interact'. This is so because households here are conceptualized as


the scene of economic allocation, arranging collectively
for the food, clothing, and sheltering of its members,
and seeking to provide for these needs over the long
term with some measure of security against the
uncontrollable disruptions of the climate, the market
economy, and the state. (Netting 1993:59).


The emphasis on the collective nature of the arrangements is important for it refers to what Netting (1993:65; Cf. Wilk 1984) calls "functional implicit contracts," which he views as holding the household unit together (for a similar argument from a feminist perspective see Kabeer 1994:64). These contracts are the implicit understandings, including both rational self-interest and social conventions, which together provide the vehicle for the integration of economic and social interpretations of behavior both within the






23


household and outside of it; i.e., employment in the wage labor market. In other words, it is within the household that the cognitive value schemas aiding householders to cognize their resources and inform= their behavioral choices operate. These schemas are constructed as a result of the discussion within and between householders regarding the household's resources and needs (for details see Rocha 1996).

In chapter five, titled "Emergy Calculations of Peasant Household Flows," a detailed explanation is given regarding how this household based model was constructed and operationalized. The resulting model integrates economic, demographic, technological and ecological factors within a similar framework allowing for their measurement with an identical metric.

My objective in developing this model is twofold.

First, with it I seek to be able to address the question of how both economic and demographic factors operate as complementary causes that induce householders to intensify their agricultural production strategies. Secondly, I hope that by employing this model the possibility of determining which of these two factors is more important at different stages during the intensification process.


1 For a discussion of the concept of householding as a mode of socioeconomic integration see Halperin (1994)
















CHAPTER 3
COGNITIVE SCHEMAS AND
NATURAL RESOURCES



Introduction


In the previous chapter I touched briefly on how several researchers have regarded the processes of agricultural intensification and increased market participation as worrisome due to the perceived effects of these on the issue of the traditional ecological rationality that is said to characterize rural householders. It is believed that these infrastructural processes--especially increased market participation--will, under specific circumstances, result in the loss of safetyfirst mechanisms that are embedded in mentally schematized models of resources that develop from the interaction of individuals between themselves and their natural and social resources. The principal reason for these worries is that, believing resource use behavior to be mediated--if not driven--by cognized models, some suspect the erosion of this knowledge will be followed by ecologically



24






25


unsustainable resource use practices with potentially negative economic consequences for long term development efforts (for details see Bedoya Garland 1992; Bellon 1995; Bennett 1993; Blaikie and Brookfield 1991; Croll and Parkin 1992; Davis 1993; Garcia-Barrios and Garcia-Barrios 1990; Guillet 1984; Gutierrez 1996; Humphries 1993; Rappaport 1977; Redfield 1991).

While the research from which these observations have been made do indeed support the idea that increased participation in the market economy by a population results in the loss, in one way or another, of "traditional" practices and the adoption of alternative "modern" ones, no research effort has to my knowledge sought to systematically analyze how this happens at an intracultural household level. By doing precisely this in my own research, I hope to offer both a methodologically reliable way of addressing the issue, as well as an empirical basis for a more detailed theoretical understanding of the causal processes that might be at work in producing the loss of traditional knowledge regarding resource use among rural agricultural producers.

In what follows in this chapter I offer a short

schematic review of how cognitive processes have figured in the theoretical models of some social science disciplines.






26


By this it is hoped that, along with the empirical results from my research, further work aimed at understanding how cognitive processes operate as proximate causes, i.e., mediating factors, of behavior will be conducted.




Cognition in Social Research


We all have, based on our own experience and intuition as social actors some--if vague--reasons to believe that the values one has in general are in some ways related to our social behaviors. More importantly, however, we also have empirical reasons to believe the same. Whenever we inquire about the why of some behavior during research we get emic accounts that seem to make causal sense only in terms of the values that actors hold. In other words, people too commonly believe that their own, and other peoples' behavior follow or are the result of, some sort of cognitive evaluation of alternatives.

Mainly during the second half of the century most social science disciplines have attempted to develop theories regarding the role of values--which are regarded as the most important dimension of human cognition--in social life. During the first half of the century, values were studied within the related fields of philosophy,






27


ethics, and aesthetics (with classic political economy being somewhat of an exception).

Within anthropology, especially during the 1950's and 1960's, as Fredrik Earth (1993:31) has suggested, the analysis of values played an important part in the efforts to understand social life. The early work of Clyde Kluckhohn (1951), for example, emphasized the role of values as psychological impulses, that, in accordance with the structure of an individual's personality, oriented behavior so that those impulses could be satisfied. Other examples from this period include Ruth Benedict's (1984) comparison of the Zuni, Dobu, and Kwakiutl societies. She organized them along a continuum of the values she believed were implied in some institutional behaviors characteristic of each society. On the theoretical side, it is worth mentioning the work of Raymond Firth (1969:208) who believed that


... the study of values, properly organized, can give
a useful systematic frame of reference for the
analysis of social behavior. [ ... ] Studies of values
help us to understand the meaning of action.


However, as time passed and many of the complexities

involved--conceptual as well as methodological--proved more difficult to overcome than some had originally believed.






28


Furthermore, a new generation of researchers moved on to other topics, and the analysis of values, if not completely ignored, was at least relegated to a corner, viewed as of secondary theoretical importance.

At first this might seem to be simply another case of a new generation, with fresh ideas and new interests, reorienting the discipline so that their theoretical and research agendas could be met; a "lets start a-new" attitude so characteristic, even if damaging, of preparadigmatic disciplines like most of the social sciences. Even if this is the case, there is, however, something more problematic behind such generational trends. Especially if one realizes that


in the explanatory realm, all action theories assume
that behavior is determined not only by constraints from the contexts in which actors are embedded, but
also [and in many cases, primarily] by actor's
evaluations of the alternative outcomes they
contemplate (Hechter 1993:1).


So, if most theories, especially those of an

idealistic kind that so characterize anthropology, assume that behavior is driven by the value orientations embedded in the cognized models of actors--implying that behavior can be explained by ideational processes, how can it be that values came to be theoretically disregarded during the






29


1970's and 1980's? I believe that Barth is right in saying that


... 'meaning' has eclipsed 'explanation' in recent
anthropology", and that "...some aspects of 'value'
have been coopted by 'meaning', while its place in the
paradigm of action has lost its salience (Barth
1993:32).


In a sense, it can be said that recent postmodern and interpretive anthropology does deal with value; it simply does it under the veil of meaning.

However, in so doing, postmodernists and

interpretativists alike disregard the original attempts of previous theoretical efforts; namely to conceptually analyze the possibility that values embedded in aggregate mental schemas can be used as means to explain--not just interpret--social behavior.

Some materialist approaches on the other hand,

especially those of a more strict behaviorist outlook (e.g., Moran 1993; Smith and Winterhalder 1992), do not offer a better solution to the theoretical problem. In fact, in being coherent with their theoretical outlook, while not denying the existence of values in particular, or cognitive processes in general, they simply do not study them. Cognition is a black-box, that some believe, is better left closed, only the systematic observation of






30


behaviors, with detailed consideration of their structural contexts and correlates, as well as their consequences, are necessary for the explanation of social processes. While in general, I believe this to be true, I also believe that the strict, orthodox, behaviorist agenda sets unnecessary a priori limits on the possibilities of scientific research of social life.

The case regarding the role assigned to cognition in sociological models is not much different than that assigned to it in anthropological ones. Max Weber (1987:22), among the first to address the issue of social values in a systematic manner, believed that among the several types of social action that are of interest to sociologists are those he referred to as "rational with relation to values", and "affective, or determined by emotional states." He furthermore argued that the only way in which researchers could attain understanding (Verstehen) of social processes was by close examination of the cognitive value systems (i.e., culture in its ideational dimension) that gave meaning to, and drove actors' behaviors (Kasler 1979:194). He believed, as many sociologists did--and still do (e.g., Giddens 1996:78ff)-that






31


... it was necessary to build a social science capable
of treating not only instrumental action, but also action motivated by values, affect, and tradition
(Hechter 1993:9) .


Perhaps the most conceptually elaborated development of such an attempt in sociology, before an interpretative turn was taken--as in anthropology--is the work of Talcott Parsons. In The Social System he develops, and builds his theoretical edifice upon the related concepts of instrumental social action, and value-oriented action; the latter understood as those actions that, in aiming to achieve a better equilibrium between an actor's gratification-privation states are informed by the shared symbols of a cultural tradition." (Parsons 1988:22-23). He furthermore believed that these concepts were not only relevant to sociological theory but also, and in a very special way, to micro-economics, since they allowed economic action to be regarded as a special case of rational, institutionally structured, social action (see Parsons and Smelser 1984).

However, as was also the case in anthropology, during the early 1970s there was a move away from these attempts to theorize about the role of cognition and values in social life as motives for behavior (see Joas 1987). Interest grew in attempts to understand the more general






32


role of symbols as meaning-giving guides to social behavior. Symbols that, being embedded in social networks, individuals could utilize in creative ways to give meaning to things and behaviors, and in turn use those to communicate intentions and desires to others (White 1993).

As has been the case with interpretative and

postmodern anthropology, this has not, however, resulted in a reexamination of the fundamental assumptions that were addressed by Weber: what "moves" people to do what they do? What is the source of social motivation? It has simply resulted in an attempt to understand (Ve-rstehen--with an abandonment of any attempt to explain) the myriad of apparently random "symbolic interactions" in which social actors, for some mysterious and unaddressed reason, engage in.

Addressing the issue of how economists deal with the

notion of values in social life, Robert Heilbroner has said that


... most economists today do not even see a need for a
theory of value, as distinct from a theory of price,
and would indeed be hard pressed to explain the
difference between the two (1988:104).


Heilbroner considers this problematic because he believes that for modern economics the concept of value is of much






33


more theoretical importance than for most other social sciences. This is so because economic theories of human behavior are based on the assumption that peoples' behaviors--and more specifically their choosing behaviors-are determined by their internal cognitive states; by the values they have and assign to the things they desire or despise (Scitovsky 1993) As Frank Knight (1969:78) has said,


the central issue is nothing less than the
question whether conscious desires or conscious states of any sort can be regarded as 'causing'
or 'explaining' conduct.


Before the Marginalist Revolution occurred in economics, the theoretical models employed assigned explanatory status to a competing set of variables, including land with it natural capacity to produce, labor and its transformative potential, and exchange with its capacity to mediate between competing sources of wealth. With the Marginalist revolution, however, these considerations fell into theoretical disuse, even if they remained useful in a descriptive sense. Under the new paradigm, that until now dominates micro-economic theory, the concept of value became a completely subjective issue (see Heilbroner 1988:126) Things are valuable not because






34


of some inherent material or structural property, but rather and quite simply, because individuals deem them valuable--in a sense, irrespective of specificities in their physical characteristics. In fact, the term value was itself dropped from almost all theoretical discourse, and was replaced by "Utility," defined as "the satisfaction derived from an activity, particularly consumption." (Rutherford 1992:480). The type of satisfaction that this concept refers to is, even if there is considerable controversy among specialists, generally agreed to be an individual-specific psychological phenomena. Different individuals, because of undefined idiosyncratic particularities, are believed to derive differential amounts of utility (i.e., satisfaction) from the consumption of similar goods and services. This notion seems to imply that, at least at an individual level, a psychological, cognitive process of valuation takes place-probably not always consciously.

Psychology too, in its theoretical efforts to explain behavior, was at first informed by vaguely defined ideational concepts that were postulated as "drives" that made individuals do what they did. As an early example of this approach there is Freud's notion of the Libido as a motivation force. He argued that located in the Id, the






35


Libido, in constant power struggles with the Ego and Superego, drove individuals to seek pleasurable sensations (Adorno 1982). The Id, Ego, and Super-ego were regarded as dimensions of an individual's "mind" that differed in that each occupied different positions along a continuum from subconscious to full consciousness. This resulted in postulating that behavior is a kind of negotiated agreement between subconscious drives and conscious evaluations of the social and moral consequences of the potential behavior. As critics soon noted, however, the concepts of Id, Ego, and Super-ego, were not easily operationalized, not to mention that the Libido concept itself was clearly metaphysical in nature and thus could not be empirically addressed.

The so-called Behaviorist approach developed as a

reaction to this, and similar types of introspective theory developments which relied heavily on complex subjective inferences by the researcher to determine "what was going on inside peoples' minds" to address why individuals did what they did. This approach originally developed within the interdisciplinary field of comparative psychology (see Vauclair 1996:5), but it soon dominated human psychology as well. Its central objective was to explain animal and human behaviors without any reference to unobserved






36


cognitive based phenomena such as "drives," "intentions,"' "desires," "wants," or "values." A lot of methodological progress was made under this approach, especially in terms of developing ways of measuring, describing, and analyzing behavior in reference only to environmental influences, past behaviors, and behavioral outcomes. Also, as a positive result of this line of research, some of the central biological mechanisms that underlay many of the most common behaviors have been described and understood, in some cases with great detail and precision.

However, many, from diverse fields within psychology, feel that the approach has reached the limits of its usefulness (e.g., see Kamil 1994; Searle 1995; Vauclair 1996). None argue that the approach is useless. Rather, they maintain that for new research to be conducted, the strict conceptual limits set to control a then young and methodologically naive discipline from over speculation, must now be removed. For example, Alan Kamil, a noted comparative psychologist, has reacted to what he regards as the empty methodological sophistication in the discipline by saying that it would seem as if


... an angry god put a terrible curse on psychology:
'You will never discover anything about underlying causal processes, and you will never understand the overlying functional significance of anything. You






37


will be forever doomed to be methodologists. You will
content yourselves with teaching each other how to do experiments, and you will never know what they mean.'
(Kamil 1994:15).


As a result of these, and other similar critiques to behaviorist models, an increasing theoretically sophisticated cognitive approach has slowly developed within psychology over the past 30 years. These developments have had positive influences on several other disciplines that have too developed cognitive approaches (e.g. Chomsky 1972; D'Andrade 1995; Rabossi 1995; Real 1994).

The cognitive approach, and this is important to emphasize, has not, and will not replace the rich behaviorist methodological tradition. On the contrary, it has built on, and will continue to build on, the methods and models developed by behavioral research. The reason is very simple: both approaches have the same goal. Namely, to scientifically explain behavior and its related biological and social mechanisms; if these include cognition as proximate causes, so be it.






38


Cognition and Anthropology


Anthropology, insofar as one of its main objectives is to account for, and explain differences and similarities that characterize present, and have characterized past human social and cultural patterns of organization, interaction, differentiation and convergence during the course of our species' evolution, cannot study phenomena at the level of individuals and their actions. Anthropologist are thus, by mandate one could say, interested in aggregate level processes; in the emergent properties that result from the interactions of individuals' and their thought processes. Thus, when studying mental phenomena such as the cognitive dimension of cultural change, methodologies that aggregate the cognitive processes of single interacting individuals have to be employed 2 The development of cognitive anthropology is to a large extent a realization of this methodological requirement (see D'Andrade 1995).

From this cognitive perspective Culture can be

regarded as "... a shared and learned information pool."


2 When studying behaviors--without considering the cognitive correlates that might be present, anthropologists rely on similar methodologies that also aggregate individual level processes; once aggregated they get labeled (e.g., mode of production, division of






39


(Romney, et al. 1986:314) D'Andrade (1981:180) suggests that this pool "... contains the information which defines what [an] object is, tells how to construct the object, and prescribes how the object is to be used." This "pool," however, is not a haphazard, random, collection of bits and pieces of "information." It is organized, it possesses an order, a structure.

There is a long history of theoretical work within anthropology that has sought to unravel just how this information is organized (e.g., Berlin 1992; D'Andrade 1981; 1995; Geertz 1973; Goodenough 1964; Holland and Quinn 1987; Levi-Strauss 1987; 1966; Rappaport 1979; Romney, et al. 1987) All of these theoretical perspectives (i.e., Interpretative Anthropology, Ethnoscience, Structuralism, Ethnoecology) maintain, somewhat as methodological working hypothesis, with some notable differences between perspectives, that language is a useful model for studying the structure of cognitive phenomena.

Linguistics, ever since the seminal work of Ferdinand de Saussure during the last decade of the XIX century, has regarded language as a hierarchically organized system of symbols. At the lowest level of analysis, as suggested by labor, class, etc.) and become theoretical constructs by means of which empirical reality gets analyzed.






40


Roman Jackobson, these symbols are constructed based on phonetically discrete, differentiable elements (i.e., sounds) which are arbitrarily assigned phonologically meaningful functions. These functions are further held to determine the form of symbols at each level in the hierarchy of language (phonological, morphological, grammatical, semantic, and discursive), while at the same time determine their meaning at the following level. This basic approach to the study of language was further elaborated by students and colleagues of de Saussure who established themselves in Prague during the 1930's.

During recent years anthropological efforts at the development of theories of cognitive processes, and no doubt in part because of critiques to traditional cognitive approaches as being methodologically unclear and impressionistic, researchers have developed formal models and techniques, that even if clearly influenced by classical linguistic theories, adhere to stricter scientific criteria of reliability, replicability, and validity (e.g., Romney 1989; Weller 1987; Weller and Romney 1988).

This new cognitive anthropology, spearheaded by Roy D'Andrade (1981; 1995), Kimball Romney (1987; 1986), Sue Weller (1987; 1988), Dorothy Holland and Naomi Quinn (1987)






41


and some of their colleagues, offers methodological techniques and theoretical models that when used in tandem with traditional ethnographic methods, can result in fresh insights for empirically studying the role of cognition in social life (for a review see Bernard and Ryan 1988).

Central to the approach is the recognition, partly empirical, partly theoretical, that


the human cognitive system [operates] as if it
were a structure seeking device. At the
appropriate level of detail, it finds which attributes of a class of instances are most
strongly correlated, and creates generic or basic level objects by forming a gestalt configuration
of these attributes. (D'Andrade 1995:120).


D'Andrade, as Rappaport had done (1979), suggests that these gestalt configurations of attributes--which he calls schemas--are hierarchically organized so that "simple schemas can be 'embedded' within more complex schemas." (D'Andrade 1995:124). Cultural schemas, furthermore, he argues ". .are built up in the course of interaction with the environment", and ". ..are abstract representations of environmental regularities." (D'Andrade 1995:122).






42


Cognitive Schemas and Resources


With regards to how the cognitive schemes employed for the understanding and subsequent use of resources that might develop in the course of humans interacting with their ecosystems, and aware that there are many potential types of and cognitive domains, Fredrik Barth recommends


... that a first step in any study of them must be to produce close empirical description of them or
their observable consequences, in the tradition of
natural history. (1993:35)


In other words, their structural properties, and domains of reference are to be regarded as a purely empirical issue, only to be determined in the field or discovered during analysis. While in principle I agree with the ""naturalist"" approach advocated by Barth, I believe that one also needs to have some theory derived idea about what to expect, especially if we want to develop models by testing hypotheses.

Work of the descriptive type advocated by Barth is not

lacking. Quite the contrary, there are many who have studied the modes of cognition people have regarding their environment. Some noteworthy examples would include the early work of Yi-Fu Tuan (1990) regarding people's love for place, and how these affective attachments develop






43


historically; Mary Louise Pratt's (1992) account of how foreigners viewed and idealized (positively and negatively) the South American lands--and peoples--they conquered; the collection of essays edited by Elisabeth Croll and David Parkin (1992) regarding environmental attitudes and perceptions as issues to be considered in development work; and Veronica Strang's (1997) recent account of how differences and similarities between cattle ranchers and aborigines in Australia's Cape York peninsula result in at times conflicting and at times conciliatory views on nature. Most of these works, being descriptive, mostly in the manner advocated by Geertz's "thick description" (1973:3-30), focus on the content of the schemes rather than on what has driven their development. They aim at giving the reader an impression as to what it means to have this or that type of world view, and the reasons (not causes) for which people say they have them.

These accounts are indeed worthwhile analytic efforts, and should accompany more formalistic accounts, for they provide an idea of the complexity and variation of empirical reality; they make the unfamiliar more understandable. They do not, however, explain the unfamiliar. They do not answer why the unfamiliar is unfamiliar, nor do they offer testable ideas regarding what might happen to the unfamiliar if some






44


of the existing conditions were to change. To address these issues formal, scientific hypotheses have to be offered and empirically tested. The work of Kempton, Boster and Hartley (1995), employing models from cognitive anthropology and based on in-depth systematic interviews, is a good example of this type of work. In it the authors analyze what are the ways in which Americans from several walks of life view and think about such issues as global warming, air and water pollution, etc.

The research reported in this dissertation is intended

to empirically address some of these theoretical issues. The goal is to understand some of the contextual factors that drive the change of aggregate cognitive models of some resources employed by rural householders. The effort is especially intended at helping to develop a better, more empirically based understanding of the driving forces in the formation of specific cognitive schemes in rural populations undergoing changes as a result of their increased participation in local market economies.
















CHAPTER 4
GENERAL METHODOLOGICAL CONSIDERATIONS





The main objective of this section is to describe how the variables that were considered of theoretical relevance were operationalized, and how the data employed for the analysis was collected, coded and pre-processed for final analysis.




Research Objectives


In response to the claims by several researchers regarding the need to better understand how "physical phenomena become absorbed into human systems of needs, wants, and profit-seeking" (Cf. Earbier, et al. 1995; Bennett 1993:473), and the suggestion by Blaikie and Brookfield (1987) that this should be done by analyzing how the types of productive activities pursued, and the physical capacity of the local ecosystem influence individuals' ways of assigning value to natural resources, I proceeded to the operationalization of several variables 45






46


that would allow me to do this among a community of peasant householders in the Peruvian highlands. Thus, the objective of the research was to analyze how peasant householders in the studied community (1) utilize their natural and social resources, and (2) how they cognize them.

In the proposal that funded this research I argued that it would be required to define, operationalize and collect data on four "macro-variables" in order to address these issues. These variables are: (1) Participation in the economic market, (2) Household composition, (3) Patterns of resource use and (4) Cognitive schemas of resources.

A detailed description of how information on the

Market Participation and Patterns of Resource use macrovariables were operationalized, coded and pre-processed is given on chapter five entitled "Emergy Calculations of Peasant Household Flows." Here the general procedures of data gathering are discussed, followed by a description of how information for the construction of the macro-variable regarding Cognitive Schemas of Resources was coded and preprocessed.

Figure 4-1 shows a schematic representation of how the main variables are conceptualy believed to be related. In






47


other words, the figure suggests the hypothetical causal relations regarding the processes studied. As can be seen







ResorceeNaura
Demgrehi ogrcsources
Copartiption SIntainabity
ine usgofReore

Figue 41. Hpotetiarla = ontions ooibl f


th m Degofuse of resources, eivdt edpneto






both demographic and economic factors--as discussed in chapter two--is regarded as the main conceptual construct that will allow for the understanding of both the intensity of agricultural production, and the manner in which resources are cognized. Sustainability in the usage of resources, in turn, is believed to be in part determined by the way in which they are both used and cognized.






48



Data Gathering Procedures;
Scope and Limitations


After an initial period of some three months during which open-ended interviews with community authorities as well as regular community members willing to participate were conducted, a more structured instrument was devised. This initial period, during which a wide variety of topics were addressed, served, thus, as an ethnographic background from which culturally relevant information could be employed as an aid for systematic data gathering. Some of this general ethnographic information is detailed in the section titled "Ethnographic Context." However, since the general objective of my research was not to construct an ethnographic picture or narrative regarding a specific aspect of community life in the Peruvian highlands, but rather to formally test a series of theory derived hypotheses, the ethnographic data gathering procedures were purposely conceived as means to construct a culturally adequate instrument for data collection.

The questionnaire that resulted from this first stage of fieldwork contained a total of 293 items on which information was gathered. Some of these "items" were of the kind that provide one unitary piece of information;






49


e.g., the name of the neighborhood where the household is, the number of plots the household has, the altitude at which the plots are located, the number of children the household heads considered ideal to have, or the weight in kilos of the members in the household. Other "items," such as free-listing and pile-sorting procedures, provide more complex pieces of information, which nevertheless refer to a single dimension of one of the four macro-variables.

Also during this first stage I obtained a copy of the most recent community census, conducted in 1990, from which a simple random sample of 40 households was selected. The community census, conducted by community authorities themselves, listed a total of 227 households. This means that if the expected prevalence of the factors under study is of 859e, and the desired precision of the statistical estimators is of 10% with a 5% probability level, then the sample is statistically representative of all community households. The important assumption here refers to the belief that the phenomena under analysis is present in 85% of the households, or stated otherwise, that about 15% of the households in my sample will not have the required characteristics--whatever they might be--for me to determine if the hypothesized causal relationships exist or not.






so


Given that there is no apriori way of knowing what the proportion of households in a population possesses the characteristics that make them appropriate for hypothesis testing, it is common to assume that one has a 50/50 chance of finding those households in a population by means of simple random sampling. That is, it is common to assume that if nothing is known of the population prior to research, the expected prevalence of the factors under study should be 50%, thus maximizing the probability of finding it, but implying that the population is maximally heterogeneous. However, and based on the general ethnographic information collected prior to the construction of the questionnaire, I feel personally confident that the households in the community are not maximally heterogeneous as a 50/50 assumption implies. A similar argument is applicable to my decision to use a 10% confidence interval for the statistical estimators, rather than a more conservative and traditional 5% one. In other words, and since anthropological research is not the type of social research where nothing is known of a population prior to the formal procedures of data gathering (see Bernard 1994b:78) I have employed less stringent sampling assumptions. Thus, for example, when I say that adult males in the community have 4.9 ( 0.8) years of formal






S1


education, it means that I am 90% statistically sure that the "true value" lies somewhere between 4.1 and 5.7, but 100% personally confident that it actually lies between 4.1 and 5.7.

If I had employed the more conservative statistical assumptions, a sample of 143 households would have been needed. Given that it took me an average of four days per household to gather all the required information to feel not only personally, but also theoretically confident I could proceed to test my hypothesis, I also adopted these less stringent assumptions for practical purposes. I would have needed some 19 months of fieldwork to collect the data, instead of the 9 it actually took.

This relatively long amount of time it took to collect the data from each household was in part due to the logistical difficulties of finding the times during which individuals of the randomly chosen households could each be available to provide the information. I wanted to have as many individuals as practically possible from each household. To have, for example, their weight and height taken by myself, or to have each describe their daily activities for me instead of relying on some other household member to describe them for me. To have as many members present at the time the pile-sorting of photos






52


showing resources used took place, so that I could feel conformable that the result really reflected a collective household view, and not that of an individual member. That is, without me seeing how they discussed during pilesorting whether "white potatoes" needed more fertilizer or were more difficult to grow than "yellow potatoes."

This long time of applying the questionnaire was also due in part to the nature of some of the information itself. I wanted to personally measure the size of their fields, the altitude at which they were located, and the amount of irrigation water they used, as well as several other related pieces of information. The alternative could have been to simply ask them about such issues, but I believe one cannot rely on peoples' recollections regarding such matters; especially, as in my case, when these pieces of information are of huge theoretical importance. Thus, I had to collect these data directly, something that is not only logistically complicated, but also time consuming.

Data regarding agricultural yields produced and sold, as well as that regarding use of labor time, however, I could not collect directly. I had to rely on their own accounts for these pieces of information. The logistical difficulties of time allocation studies (see Bernard 1994b:321; Gross 1984) would not have allowed me to collect






53


any other type of data other than how the people in each household employed their time. A similar situation characterized my attempts to weigh their yields, and the fraction of those they sold.

Thus, the way these two important pieces of

information were collected was as follows. To get information on each of the household's members use of time, I first asked them to describe as completely and accurately as possible what they had done the previous day. Then I asked them if they considered "the previous day" to be representative of a "typical" day. Following this question, and regardless of how they answered it, I asked each of them to describe a "typical day" for them. Finally I asked them to tell me how much time they would normally allocate for a series of activities on a "typical day." In other words, I estimated for each household member their time allocation strategies based on three sources of information. In all cases the information matched up quite well, making me feel confident that the amount of measurement error is as small as can be given that it was not a direct way of estimating how time was really spent. A similar strategy was used to gather information on their yields and how much of those they sold. I simply asked each of the household members, separately when possible,






54


how much of a given product they had harvested the previous season (between two and four months before I began the interviews), and how much they had sold. I then asked the women in the household--who are regularly in charge of kitchen and household duties--how much of each of the crops they produced they had kept from the last season's harvest. I then compared these three pieces of information, which again, matched-up very well, and used the averages of each as the best possible estimate of the true measures.

Thus, as a whole, I feel confident I did as much as practically and logistically possible to minimize measurement errors on all items on which information was collected. This applies both to emic accounts as well as etic observations and measurements.




Cognitive Schemas of Resources Data


In order to collect information that would allow me to construct formal models of each household's cognitive schemas of resources, I relied on Weller and Romney's (1938) data gathering procedures. After the initial phase of un-structured interviewing, during which I determined what resources I would focus on for analyzing these cognitive schemas, I went to a local market and took photos






55


of 34 different agricultural food products, and 7 types of fertilizers. Then at several places I took photos of 12 different types of soils, trying to maximize the variability of these. These 3 sets of photos were finally given to as many household members as I could, so that--as I said above--a group view resulted, and asked them to sort them into piles based on several criteria.

For the 34 foodstuff photos I asked them to first sort them into five piles based on how difficult they where to take care of while growing. I then asked them to sort the same photos again into five piles, but now based on how much fertilizer they required in order to provide good yields. With the fertilizer types and the soil type photos, a similar task was performed but requiring only three piles, given the smaller number of items. For these two sets I asked them to sort based on how good they were for growing potatoes.

Given the way in which I asked them to do the pile sorting, that is, by giving them an explicit criteria rather than allowing for "free sorting," I could process the results in two ways. One was as traditional pilesorting in which the measure of similarity between items is the result of how many times each item ends up on the same pile for each of the informants. The other way in






56


which I could process the data was as similarity ranking of the items sorted. That is because, for example, with the soil types I asked them to sort into three piles, each of which would have soils that were regarded as increasingly better for growing potatoes. In this way the measure of similarity between each item is the result of the positive matching between each respondent's profile matrix.

After carefully analyzing and comparing both

procedures, which provided extremely similar results (e.g., the correlation between both types for the agricultural foodstuffs data regarding their difficulty for growing is

0.994), 1 decided I would only employ the similarity rankings for two reasons. One is simply that they are a more direct way of measuring what I wanted to measure; that is, how similar the items were considered to he given a specific criteria, and not in a general way. Secondly, because there is a much higher probability of finding a pattern of agreement between each household's profile wh-en rankings are used than when pilesorts are employed. This is because, for example, using the larger set of photos, only 34 items are compared with the rankings, while (34(341))/2 = 561 are used with the pilesort S3.


3 With pile sorting the number of similarity measures that results, and which can then be used for other analytic procedures such as






57


Thus, because I needed to estimate the degree of

cultural consensus (Romney, et al. 1987; Romney, et al. 1986) between households regarding these domains of knowledge, I employed rankings as both being more practical as well as adequate. The analytic technique of cultural consensus not only allows for the estimation of how much consensus exist on a given cultural domain of knowledge, but also, and based on this consensus, estimates the degree to which each of the units of analysis--in my case households--shares in that knowledge. In other words, with it I could estimate how knowledgeable each of the household's members are regarding these cultural domains.

This estimation of shared cultural knowledge for each of the households for each of the four domains I collected information on (crop difficulty, crop fertilizer amounts, fertilizer types and soil types) are extremely important for determining if different patterns of resource use and different degrees of market participation result in changes regarding how resources are cognitively schematized.

Finally, once the cultural consensus based knowledge on each of the four domains was estimated for each household, I constructed a Likert scale (McIver and consensus analysis, is determined by the number of items sorted; N(N1)/2, where N is the number of items.






58


Carmines 1981) that summarized all knowledge domains along a single dimension.

It is important to note here, even if I will discuss this issue further when results are presented, that the data regarding fertilizer types best suited for growing potatoes, did not yield a good consensus. A good consensus is yielded when a single factor explains a larger portion of the observed variance; when there is no consensus, that is when there is no agreement between units of analysis, two or more factors contribute to explain the variance. Basically this means that there are two or more sets of ideas regarding what types of fertilizer are best for growing potatoes. When such is the case, one cannot, for obvious reasons, estimate how culturally knowledgeable individuals are: there is more than one culturally coherent way of being right.

Thus, given that the knowledge estimates I have for this cultural domain are not reliable, I did not include them on the summarized Likert scale of cultural knowledge. Only the other three for which I had technically reliable estimates where employed (i.e., crop difficulty, crop fertilizer amounts and soil types). The resulting Likert scale fits the data well since it has a Cronbach's Alpha of 0.846, and a single factor explaining 100% of the variance.






59


This measure, Cronbach's Alpha, is a reliability indicator of the constructed scale, and ranges from zero to one with higher numbers indicating higher reliability.
















CHAPTER 5
UNDERSTANDING EMERGY CONCEPTS AND CALCULATIONS





Since some of the figures reported throughout this

thesis are in energy units, it is important to be able to understand the basics of how they are calculated and what they mean.

H.T. Odum defines ENERGY as "the available energy of

one kind previously used up directly and indirectly to make a service or product. Its unit is the emjoule". Following this he defines SOLAR ENERGY as "the available solar energy used up directly and indirectly to make a service or product. Its unit is the solar emjoule (abbreviated sej)" (1996:7-8) Thus, as he also says, "emergy .. measures both the work of nature and that of humans in generating products and services." (1996:1).

There is an important difference between the concept of energy and that of emergy. Energy is traditionally defined by physicists and thermodynamicists as the physical potential to do work, and is measured in heat units.



60






61


Emergy, on the other hand, as is noted by the definitions above, is itself defined in terms of energy, but a "Tqualitative" spin is given to it. Odum argues that the physicist's definition of energy is too narrow and has become distanced--by its technicalities--from the "common sense" understanding of energy. With the concept of emergy he hopes to recapture what use to be the original--naive-idea behind energy; if something has more "energy" then it is more "valuable" (Odum 1996:13-14).

As an example consider a normal pencil. It is made of wood, graphite, a little metal around the eraser, and the eraser itself from rubber. In a physical sense, if one wanted to know how much energy is obtainable from the pencil (i.e., how much energy it has), one could burn it and measure the amount of heat it releases. Let's assume that, when burned, it produces 600 calories of heat (one calorie is the amount of heat needed to raise one cubic centimeter of water one degree Celsius at sea level); or its equivalent in JouleS4 2,512J. That is the energy of a pencil. Once it has been transformed into heat--a form of energy with the highest level of entropy, making it 4' One joule is the work done when a force of one newton moves something a distance of 3.3ft. (1 meter) in the direction of the force. Since it is a mechanical equivalent of heat (i.e., calories; 1 Joule =






62


impossible to reuse--one cannot make any use of the energy released. Its energy, however, is something else. In order to calculate the energy of a pencil one would need to know how much energy has been transformed (i.e., used up) in the making of the pencil. The total amount of energy that has gone into the making of the pencil is its energy. Thus one would need to know how much energy was needed by the cedar tree from which the wood was taken (and consider only the fraction from the pencil itself), how much energy it took to make the graphite (regardless of whether it was naturally or industrially made), etc. Also, we would need to know how much energy was needed to make the machines needed to make the pencil (again, only counting the infinitesimally small fraction of wear in them for producing just one pencil!), the amount of energy needed to sustain the labor of those working in the production, etc., etc. Then, when we know all of these sources of energy, we add them up and the result will be the energy content of a pencil.

As can be expected, the energy of a pencil will be much higher than its energy, simply because it will have taken into account all the energetic inflows (or fractions


0.238846 calories) it has been adopted as an international standard for measuring the work done by energy.






63


of them) that were needed to end up with the pencil. Furthermore, as can be imagined, calculating the emergy of something as simple as a pencil, can be a very complicated and time-consuming task! However, since the emergy of many natural and human products have been calculated, these results can--and indeed, must--be used to estimate the emergy of other things in which these where involved in some ways. These intermediary calculations are known as T-ransformities (Odum 1996:10), and represent how nuch the energy of something has been transformed form one thing to the next. Our pencil would have a higher transformity than the wood from which it was made, and the written paper that used up the pencil's emergy would, in turn, have a higher one still!

Now, because this way of accounting for the

transformation of energetic flows necessarily involves "different" kinds of energy (i.e., energy from natural processes, from human labor, etc.), a common standard for comparison is needed. This is provided by the Sun and its radiated energy, source of most of the energy on the earth's ecosystems 5. That is why for comparative purposes units of sola-r eme-rgy are used. SThis standard, like most standards, is only a convention good for practical purposes. The energy produced at the core of the earth,






64


Now, the biggest advantage of using energy

calculations over energy ones is quite simple. It allows for one to compare between natural processes and social ones using the same computational procedures. It is possible, then, to determine how much of the work 6 that was needed for the making of our hypothetical pencil came from nature alone, and how much from humans and their efforts in changeling natural flows to their benefit.

For ecological anthropology and its objective of

studying the interactions between humans and the ecosystems in which they carryout their activities this is indeed a both a powerful and useful tool. It helps to overcome past critiques which argued that "not everything is calories." Or in other words, as Odum would say, energy and its technical definition is too narrow for the understanding of most human-ecosystemic processes. Furthermore, because energy accounting assumes a hierarchical organization of natural processes (Odum 1996:15), it also allows the clear visualization at a micro-level of what Leslie White (1982) conceived as the increased efficiency in the utilization of energetic flows that are naturally available. Since in which is independent of the sun, could have, for example, also have been taken as the conventional standard.






65


order to have access to the benefits of an energy (i.e., resource) flow, humans--just like all other living organisms--must make use of some energy themselves (which can be stored in their bodies, tools and technologies and/or information), the ratio between used energy and accessed energy must be higher than 1 in order for the effort to be worth while, otherwise more energy would have been spent than gained. It is important to note that this excludes emergy spent on such things as agricultural rituals, etc. If emergy is considered, the energy in the tools used will, most of the times, have a higher transformity than that form which a benefit is sought. In other words, the tools' emergy will be "more concentrated" and of a "higher quality" than the one from the flow being exploited. This implies that higher order biological organisms usually make use of small amounts of "concentrated energy" to have access to large amounts of "dispersed energy". This idea is very similar to the one White implies in his description of cultural evolution as the result of increased energetic efficiency, with the huge advantage that it can be measured systematically (for a




6 Work, here understood in the physical sense as the consumption of energy.






66


discussion on the importance of measurement regarding these types of approach in anthropology see Adams 1988).
















CHAPTER 6
ENERGY CALCULATION OF
PEASANT HOUSEHOLD FLOWS



The Theoretical Objective


Peasant households, viewed as productive social

institutions, have several characteristics that make them function differently from urban households. These characteristics are mainly a result of the ways in which their productive functions are structured around economic and ecological energetic flows. In contrast to most urban households, peasant householders interact, to sustain their livelihoods and that of others, in a direct manner with the environmental processes that sustain all biological life. In tilling their fields, tending for their crops and animals' many biological needs, and bringing a portion of these efforts to the market in the form of agricultural products, they transform the environmental landscape and harness a portion of its energetic flows into the sphere of the social.





67






68


The ways in which individual households are interwoven both to the ecological landscape and the social economy, are indeed complex and varied. There have been many researchers who have developed theoretical models in an attempt to conceptualize and measure these complex interactions. Based on insights from many of these models (e.g. Berry 1993; Boserup 1965; Cancian 1991; Chayanov 1986; Cook 1975; Dalton 1961; de Janvry 1931; Godelier 1970; Halperin 1994i Hamaza 1987i Kearney 1996; Meilla~aoux 1973; Mintz 1989; Netting 1974; 1993; Orlove 1930; Sahlins 1972; Scott 1977; Wilk 1993b; Wolf 1982), and employing the emergy evaluation procedure developed by H.T. Odum (1994; 1996), 1 here offer a way of systematically measuring the contribution of both ecological and economic energetic flows that sustain a peasant household, and indirectly-through their participation in local markets--that of other both peasant and non-peasant households. Also offered is a way of assessing, with several ratios, the intensity with which environmental resources are utilized, the degree of market participation of a peasant household, and the overall sustainability of a specific peasant economy.

In so doing, an attempt has been made to consider and integrate what I regard as the most important theoretical considerations proposed by several authors (op. cit.)






69


concerning peasant households economic-ecological processes (for details see Rocha 1996; 1998) These are basically two: (1) Peasant households are more directly interwoven to the local environment than other types of household (i.e., urban households); and (2) they are less completely interwoven with, or dependant on, economic market processes than other types of households

It is assumed, for theoretical reasons, that these two dimensions are probabilistically more important than others (e.g., ethnic identity and/or composition of the household, political or religious affiliation, structural linkages to other households, etc.) for explaining the diachronic functioning of peasant households. In other words, it is argued that the proposed methodology incorporates the most important theoretical considerations regarding the idea of probabilistic infrastructural determinism, while also providing a systematic way of measuring its many dimensions.








7The political dimensions of these differences while extremely important for understanding such things as are peasant's discourse, identity, power relations with the rest of society, etc. are more likely--from this perspective--a result of the differences themselves than their causes.






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The Methodology


In the previous chapter I outlined the general

objective of the Emergy Analysis Methodology as proposed and developed by H.T. Odum. It is important to state that this methodology is not theoretical. It was developed in order to be able to study ecosystems and their complex interactions systematically. One of the sets of interactions of natural ecosystems includes those that link them to human social systems. Like most methodologies of the Natural Sciences, it has a positivistic outlook and is grounded on the belief that scientific knowledge is more successfully attainable by the careful examination of empirical reality in its natural setting. Furthermore, and insofar as it is influenced by a physical understanding of relationships between phenomena, it has a mechanistic as well as materialistic orientation to the way in which variables are conceptualized.

Because in my research I have adopted a positivistic, empiricist perspective, there are no conceptual contradictions between the methods employed and the theoretical position employed. However, I have adopted a probabilistic perspective regarding the relationships between the variables to be analyzed. Thus, the






71


mechanistic nature of emergy analysis is dropped, and replaced by a statistical approach to the study of relationships between variables. This strategy implies more computational work, for as will be seen, it requires that calculations be performed for all units of analysis instead of on a single theoretical construct commonly referred to as "the system." (see Rappaport 1990) The way in which I have proceeded here is to treat each sampled household as "an empirical system," the modal type of which, as a construct, can be called "the system."















wellas iavnags ThTmjroiadatag istemc

l a r e r q u a t i y F cal l a t onwt a h ae to e p r f o m e d

several hundrepe uit ofaayi. TeBao datg






72


is the possibility of providing empirically based standard error estimates for these calculations.

Figure 6-1 shows the general systems diagram from

which the computational model is developed. It represents a graphic view of how it is theoretically believed that a peasant household is structured around its mayor energetic flows. As such, an effort was made to incorporate into this ideal type model all the important dimensions of peasant households discussed in the reviewed literature. The model was created on an excel worksheet so that the calculations for each sampled household could be easily made, and then statistically analyzed. Table 6-1 gives the results of these calculations for a modal Tumpa Household. Note that this table represents no -real household as such, but rather a conceptual construct based on the most common type of household one can find in Tumpa.

The table is divided into four general sections

arranged in rows. The first consists of environmental flows, and it is itself divided into two subsections depending on whether the resources considered are renewable or slowly renewable, corresponding to flows Ea and Eb on figure 6-1. The next section considers those flows that are of an economic nature, i.e., acquired by the household by means of market exchange, shown on the diagram as F,






73


with a fraction of them being used for production and another part being subsistence as the line going into the household icon suggests. Clearly this is an important distinction for it has been noted by most authors who have studied peasant households that peasants not only acquire productive inputs from local markets, but also purchase foodstuffs for their own consumption (e.g. Wolf 1982).

Following that section an evaluation of the

household's stocked energy is given, Q on the diagram. This stock is the amount of energy that a household keeps from one year to the next for whatever purposes it may then serve. Finally, and also divided into two subsections, the energy yields of the household are calculated. Because peasants keep some of the fruits of their labor for their own consumption and sell only a fraction of the total produce, both internal (Y1) and external yields (Y2) are given. This characteristic--i.e., having both an internal and external yield--is very important for understanding peasant households. It is one of the ways in which I have incorporated into the model the theoretical discussions regarding the double economic nature of peasants. The fact that they are both, in different degrees and under specific situations, subsistence and market oriented producers.






74


On the columns, the first is the number that refers to the table's footnotes where the formulas for the calculations are given, the second is the name of the item considered. The third gives the raw results from the calculations, most of them are in Joules, and would be comparable to traditional energy analysis methodologies (e.g. Hannon 1991; Moran 1979). The next column shows the transformities used for the calculation of the emergy content of each item considered; as explained on the previous chapter transformities are the results of previous research by other scientists who have had the need to calculate them in their own research. On my own research I had the need to calculate the transformity of glacial runoff water since none existed (see appendix for details)8 Next to that column is the one which gives the results of multiplying the raw units by the transformities, which amounts to the total solar emergy content of that flow in a year's time. On this column, for ease of comparison, all numbers are given on an 1E+15 standard. Thus, for example, on an average Tumpa household, there are 0.30 E+15 solar emjoules (sej's) of emergy from the chemical potential of



8 1 have given the calculations to Dr. Mark Brown who considered them adequate, and will be included on his data base of existing transformities so that others can use it.







75





Table 6-1. Emergy Flows of a Modal Tumpa Household Solar Emos
tRaw Transformity r EmSoles
Note Item Units (sej/unit) Emergy 1E+03
1E+15


Renewable Sources:
1 Sunlight 9. 11E+13J 00E+00 0.09 0.05
2 Rain, Chemical 1.67E+10 J 1.82E+04 0.30 0.16
3 Rain, Geopotential 3.40E+10 J 1.05E+04 0.36 0.18
Slow-renewable Sources
4 Soil lost to erosion 3.29E+06 g 7.40E+04 0.00 0.00
5 Glacial Flow, Chemical 1.09E+12 J 8.92E+04 97.55 50.11
6 Glacial Flow, Geopotential 2.89E+10 J 4.82E+05 13.95 7.17
7.a Animal Food (Internal) 1.56E+08 J 1.30E+06 0.20 0.10
7.b Vegetable Food (Internal) 8.34E+09 J 6.80E+04 0.57 0.29
8.a Household Labor 1.58E+09 J 1.52E+06 2.40 1.23
Total Environmental Emergy = 101.02 51.90


Inputs; Goods and Services
8.b Household Labor 1.18E+09 J 1.52E+06 1.79 0.92
9 Hired Labor 6.68E+07 J 1.52E+06 0.10 0.05
10.a Animal Food (External) 4.82E+08 J 1.30E+06 0.63 0.32
10.b Vegetable Food (External) 5.84E+09 J 6.80E+04 0.40 0.20
11 Animal Feed 1.47E+09 J 6.80E+04 0.10 0.05
12 Fertilizers 8.41E+08 J 2.OOE+06 1.68 0.86
13 Tools 9.03E+03 g 6.70E+09 0.06 0.03
14 Money Loans 2.40E+02 S/ 1.95E+12 0.47 0.24
Total Economic Emergy = 5.22 2.68

Biotic Stock
15 Humans 8.48E+08 J 1.52E+06 1.29 0.66
16 Animals 1.72E+09 J 1.30E+06 2.24 1.15
17 Crops 4.71E+10 J 6.80E+04 3.20 1.64
Total Stocked Emergy = 6.73 3.46

Internal Yield (consumed)
7.a Animals Consumed 1.56E+08 J 1.30E+06 0.20 0.10
7.b Crops Consumed 8.34E+09 J 6.80E+04 0.57 0.29
Total Internal Emergy Yield = 0.77 0.40

External Yield (sold)
18 Labor Sold 7.07E+08 J 1.52E+06 1.07 0.55
19 Animals Sold 5.57E+08 J 1.30E+06 0.72 0.37
20 Crops Sold 3.36E+10 J 6.80E+04 2.28 1.17
Total External Emergy Yield = 4.08 2.10

Overall Emergy Yielded = 4.85 2.49






76


rain per year. If one looks at note 2, to see how this figure was calculated, one sees that it is mainly a function of land area; more area of land, more rain captured by the household system. Finally the last column gives the macro-economic value of each flow (for ease of comparison all figures are in 1E+03 units), obtained by dividing the emergy of each flow by 1.95E+12 sej/Sol. This figure itself was obtained by dividing all the emergy flows of Perti in 1996 by that year's GNP. Thus, this last column gives an estimate of how many Soles would be needed to pay for each emergy unit of that flow (for details see Odum 1994) Assume, for example, that the modal Tumpa household decided to pay for the 0.30 E15 sej it gets freely from rainfall, the economic cost of that amount of emergy based on the national average price of emergy would be 0.16 E03 soles, or 160 soles. Now, it is clear that one cannot pay anyone for the rain one receives; that is why it is considered a "free resource." However it is not free in the sense that no work was performed to provide the resource; the natural ecosystem went trough a series of "tasks" during which energy was used-up and transformed into rain. That used-up energy that has been transformed into rain, is the EMERGY of rain. If one were to pay someone for an equivalent amount of water to be delivered






77


to the site of this household (at about 3,000 meters above sea level), a good estimate of how much it would cost based on the energy that someone would use in the delivery process, is 160 Peruvian soles, or about $53 US dollars.

Based on the results of this table there are a series of established ratios that can be employed for the evaluation how natural resources are used (see Odum 1996). others have been constructed based on the objectives of my research. These ratios are given on Table 6-2, and are based on the results for the modal household on Table 6-1. There are three general types of ratios on this table, the calculations of which are based on the definitions shown which refer to the flows on the system's diagram on Figure 6-1. The Unit of Measure column provides a descriptive explanation of what each of the measures mean.

The first Agro-economic ratio is pretty simple since it gives the relation of fallow to productive land, which on a modal household in Tumpa is 0.29. The next two ratios refer to how much labor or technology derived energy units are employed per unit of land. Thus, for example, one can see that a modal household employs more labor than technology derived energy for its agricultural production process. These two ratios can be viewed as ways of assessing the two different intensification production






78


strategies available to peasants. Netting (1993:108) has

argued that intensification as Boserup (1965)

conceptualized it refers to labor intensification and that

contrary to what many agricultural economists--e.g.,

(Ruttan and Hayami 1990; Timmer 1990)--have argued this

form of intensification is more important than technology

based intensification for understanding the structural

transformation processes of peasant households. In the

sections where the analytic results of my research are

given, I will provide the results of empirically testing

these competing hypothesis. Here it is sufficient to say


Table 6-2. Evaluation of Resource Intensity Usage Ratios Definition Result Unit of Measure
Agrzoeconomic;
Fallow Hectares of Fallow Land for
Cycle B / A = 0.29 every hectare of Productive Land
Labor E+15 sej of Labor for every
Intensity L / A = 9.89 hectare of Productive Land
Technological E+15 sej of Technology for every
Intensity F / A = 4.12 hectare of Productive Land
Demographic;
Density to Individuals for every
Productive Land Q / A = 14.16 hectare of Productive Land
Density to Individuals for every
Fallow Land Q / B = 48.17 hectare of Fallow Land
Overall Individuals for every
Density Q / A+B = 10.95 hectare of Land
Environmental;
Environmental E+15 sej of Environment for every
Intensity Ea+Eb / A = 231.00 hectare of Productive Land
Emergy F+L / Sej yielded to Economy for every
Yield YI+Y2 = 1.22 socially inputed sej
Sustainability F+L / Sej socially inputed for every
Ea+Eb = 0.06 Environmentally inputed sej






79


that a higher labor intensity strategy is more prevalent than a technological intensity one on Tumpa households.

The Demographic Density ratios are the standard individuals per hectare of land measures used in agricultural demography. These provide an idea of how much each household uses its land resources based on the number of members it has. On a modal household there are about 14 individuals per hectare of productive land, and about 11 for every hectare of land the household has. This implies that most households have relatively small amounts of land from which they satisfy a part of their needs. In fact, the average amount of land a household has in Tumpa is a mere 0.53 ( 0.06) hectares.

Finally, the environmental ratios serve to assess the manner in which ecological resources are employed. The first ratio--environmental intensity--indicates how much emergy from the environment is employed for production, this being related to both the amount of land, irrigation, and rainfall. As can be seen, when compared to either the technological or labor intensity ratios, the figure of the environmental intensity ratio is much larger. This simply shows that for the typical household in Tumpa more work is being done by the natural environment than by humans. If a mechanized agriculture type of system were the one that






80


characterized the Peruvian rural landscape, it would be expected that the technology intensity ratio be almost equal to the environmental intensity one. The emergy yield ratio provides an indication of how much emergy each household obtained for every unit of emergy that was inputted, the inputs being labor, fertilizers and tools. Thus, as can be seen, the average household has a positive yield of 0.22 solar embodied joules of emergy. The last environmental ratio, the sustainability ratio, assesses how locally sustainable the productive practices of each household are. In order to say that the household system is "locally sustainable"!, the ratio would need to be equal to one, since this way the amount of emergy from the environment is equaled by a similar amount brought in from the outside. Otherwise the local pool of resources is being depleted much faster than it would if matching resources from outside where employed. Because of the huge dependence on glacial waters, and their high energy content, all households have less than unitary ratios, indicating a big dependence on the environment for all households. If, for example, irrigation was mechanized this ratio would be closer to one, or probably higher--but others such as the technological intensity ratio would also go up.








81




The overall objective of this conceptual and


methodological effort is to provide an empirical basis for


systematically and formally analyzing the several ways in


which individual peasant households are articulated both to


their immediate natural environments and their local market


systems. It is hoped that in so doing more reliable


estimations can be made regarding what factors underlay the


differences and similarities of households' use and


conceptualization of their available resources.







Notes to Table 6-1.

1 Sunlight;
Total Area (m^2) = 5.48E+03
Insolation (kcal/cm^2/yr) = 567.259 (Covis Satellite Data 1989)
Albedo = 30.00% (Covis Satellite Data 1989)
Total Energy (Joules) = (area)(avg. Insolation) (1 Albedo) (lE+4 cm^2/m^2) (4186.8 J/kcal)
= ( m^2)( kcal/cm^2/yr)(1 %) (lE+4 cm^2/m^2) (4186.8 J/kcal)
= 9.113E+13
2 Rain, Chemical Potential;
Total Area (m^2) = 5.48E+03
Rain Fall (m/yr.)= 0.82 (ElectroPeru 1988:78)
Evapotranspiration = 75.00%
Total Energy (Joules) = (area)(rainfall)(evapotranspiration)(Gibbs No.) = ( m^2)( m) ( %)(lE+6 g/m^3)(4.94 Jig)
= 1.67E+10
3 Rain, Geopotential;
Total Area (m^2) = 5.48E+03
Mean Elevation (m) = 3087.5
Rain Fall (m/yr.) = 0.82
Runoff rate = 25.00% Total Energy (Joules) = (area) (rainfall) (runoff) (avg. elevation) (gravity)
= ( m^2)( m)( %)(lE+3 Kg/m^3)( m)(9.8 m/s^2)
= 3.40E+10
4 Soil erosion;
Deposits at System's End (g) = 6.84E+12 (Electroperu 1988:116)
Area as % of total = 0.00007% Area of 7.97E+09
Callejon (m^2)
Estimated Top Soil in Deposits 70.00%
Total Soil lost (g) = 3.29E+06








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5 Glacial Flows, Chemical; Total Water (m^3) = 5.90E+05 Runoff = 50%
Evapotranspiration = 75% Water used = 2.E+05 Total Energy (Joules) = (volume)(1 E+6 g/m^3)(4.94 J/g) = 1.09E+12
6 Glacial Flows, Geopotential; Flow (m^3/yr) = 5.90E+05 Average height of river entry 3090
(m) =
Height of river egress (m) = 3085
Total Energy (Joules) = (flow volume) (density) (height of entry height of egress) (gravity)
= ( _m^3)(lE+3 kg/m^3)( m)(9.8m/sec) = 2.89E+10
7 Human Food (Internal);
7.a Animal food consumed (g) = 1.24E+04 Assuming 55% water
Energy from animal food (J) = ( grams) (12560 J/g) = 1.56E+08
7.b Vegetable food consumed (g) = 4.06E+05 Assuming 60% water
Energy from vegetable food (J) (___grams) (20520 J/g)

= 8.34E+09
8 Household Labor; Adults 8.62 hrs/day Children 1.04 hrs/day Total hrs. per year 1880.83
Total energy (J) = (Hrs.)(350 kcal/hr)(4186 J/kcal) = 2.76E+09
% sustained by environment = (Emergy from internal food) / (Emergy from all food) = 57.32%
Sa Environmentally sustained 1.58E+09
Labor (J) =
8b Economically sustained 1.18E+09
Labor (J) =

9 Hired Labor; Total hrs. per year 45.60
Total energy (J) = (Hrs.)(350 kcal/hr)(4186 J/kcal) = 6.68E+07
10 Human Food (External); 10.a Animal food consumed (g) = 3.84E+04 Assuming 55% water
Energy from animal food (J) = ( grams) (12560 J/g) = 4.82E+-08
10.b Vegetable food consumed (g) = 2.85E+05 Assuming 60% water
Energy from vegetable food (J) (__grams) (20520 J/g) = 5.84E+09
11 Animal Feed; Animal Feed (g) = 7.18E+04 Assuming 40% water Total energy (J) = ( grams)(20520 J/g) = 1.47E+09
12 Fertilizers; Fertilizers Used (g) = 2.01E+05
Total energy (J) = ( grams)(4187 J/g) = 8.41E+08







83




13 Tools;
Total tool weight (g) = 4.51E+04
Portion used annually (g) = 9.03E+03 Assuming a five year average replacement time
14 Money Loans;
Amount Received (Soles) = 240 15 Humans;
Household Members = 6
Total = 6
Dry weight (g) = 6.75E+04 Assuming 45kg average, & 75% water
Total Energy (J) = (_grams) (12560 J/g) = 8.48E+08
16 Animals;
Cattle (g) = 5.68E-404 Assuming 150kg average, & 60% water Sheep & Goats (g) = 3.70E+04 Assuming 60kg average, & 60% water
Pork (g) = 2.79E+04 Assuming 40kg average, & 70% water
Chicken (g) = 3.55E+03 Assuming 2.5kg average, & 50% water
Cuyes (g) = 1.19E+04 Assuming 2.0kg average, & 50% water
Total dry weight (g) = 1.37E+05
Total Energy (J) = (_grams) (12560 J/g) = 1.72E+09
17 Crops;
Cereals (g) = 3.33E+05 Tubers (g) = 9.71E+05 Vegetables (g) = 9.86E+05 Total dry weight (g) = 1.15E+06 Assuming, Cereals 20% water;
Tubers 60% water, Vegetables 50% water Total Energy (J) = (_grams) (20520 J/g) = 4.71E+10
18 Labor Sold;
Labor Sold = 4.76 Hrs/day Total hrs. per year 482.51
Total energy (J) = (Hrs.) (350 kcal/hr)(4186 J/kcal) = 7.07E+08
19 Animals Sold; Cattle (g) = 1.50E+04 Assuming 150kg average, & 60% water
Sheep & Goats (g) = 1.88E+04 Assuming 60kg average, & 60% water
Pork (g) = 7.14E+03 Assuming 40kg average, & 70% water
Chicken (g) = 7.09E+02 Assuming 2.5kg average, & 50% water
Cuyes (g) = 2.68E+03 Assuming 2.0kg average, & 50% water
Total dry weight (g = 4.43E+04
Total Energy (J) = ( _grams) (12560 J/g) = 5.57E+08
20 Crops Sold;
Cereals (g) = 2.28E+05 Tubers (g) = 5.69E+05 Vegetables (g) = 8.15E+05 Total dry weight (g) = 8.18E+05 Assuming, Cereals 20% water;
Tubers 60% water, Vegetables 50% water Total Energy (J) = (__grams) (20520 J/g) = 3.36E+10
















CHAPTER 7
ETHNOGRAPHIC CONTEXT



Tumpa; A Peasant Community


The community of Tumpa (09011'28"S; 77'44'39"1W;

3,O75mts), located on the western slopes of Peru's highest mountain---Nevado Huascardn--on the Callej6n de Huaylas is a typical north-Peruvian highland peasant community in many respects. In other respects it is quite different even from neighboring communities. It is these small differences, especially as they apply internally, between community members themselves, that constituted the main research objectives of this dissertation. The idea is that by systematically analyzing them one can achieve a better understanding of what the causes of social, economic and cognitive differences are that arise as a result of market integration. Thus, in order to provide the reader with a sense of ethnographic context, it will first be useful to describe what can be said to constitute the broad outlines of community life.




84






85




Regional Geographical Context


The community's central plaza location at an altitude of 3,O75mts places it at the lower limits of the ecological niche know as "Quechua" (Pulgar Vidal 1946)9. From this

altitude up is where most of the community's lands are held. This niche, just above the "Yunga" zone which is the most productive for agricultural purposes in the highlands, can be quite productive if sufficient irrigation is available. In the case of Tumpa this is no problem. The glaciers of the Cordillera Blanca to the east, especially those of Nevado Huascardn, provide sufficient water throughout the whole year. Only a few fields on the community, because of topographic anomalies, do not have irrigation and thus have to depend on rainfall. Based on my sample, some 94.6% ( 3.8%) of the fields owned by members have access to irrigation from the glaciers. This means that in contrast to other communities in the region, such as those living on the Cordillera Negra just some 6km across the valley who do not have any glacier irrigation,



9Pulgar Vidal developed a typology of eight "natural" ecological zones in the Andes based on both the ethnoecological knowledge of andean populations, and the environmental characteristics of these culturally recognized zones.






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in Tumpa householders can harvest most of their crops twice a year rather than only once.

These regional ecological differences between areas with no glacier irrigation and those for whom it is available have huge effects on settlement patterns, use of resources, general productivity, and economic well-being. Based on Peruvian census data as well as other sources (INEI 1997; Electroperu 1984:116; Hidroandina 1982:36), and using H.T Odum's emergy analysis methodology (1994; 1996), Table 7-1 can be produced. It provides several important indicators for assessing these


Table 7-1. Regional Environmental Differences
Index BLANCA NEGRA
Environmental as % of total flow 97.98% 51.26% Imported as % of local flow 0.59% 31.40%
Emergy Yield Ratio 0.57 0.68
Empower Density (Emergy per m2) 1.66E+12 7.06E+10 Environmental Emergy per Capita 5.69E+16 1.85E+15 Economic Emergy per Capita 1.18E+15 1.76E+15
Demographic Density per km2 28.59 19.53


regional differences. It can be clearly seen that the opposed semantic meanings (Negra = Black, Blanca = White) employed to designate the two Cordilleras of the Callej6n de Huaylas also show similar contrasts on most of these analytic indicators.

For example, based on the first indicator, one can see that the amount of environmental emergy as a fraction of






87


the total emergy of the system is much larger for the Cordillera Blanca. This should be no surprise since, as I said, it is the Cordillera Blanca which has year round glacial waters available. These glaciers are formed from the annual precipitation that accumulates on its relatively higher peaksl10 This fact is also reflected on the fourth and fifth indicators which measure the amount of energy per unit of land, that is, how densely emergy is geographically distributed, and how much is available per individual in each system.

From the second indicator--energy imported as a

fraction of the one that is locally available--one sees that the amount of emergy that is imported into each of the systems in order to make them agriculturally productive is also very different. In the Cordillera Negra there is a much higher need to, one can say supplement, the available sources of emergy by making more use of external ones such as fertilizers, tools, as well as the building of an irrigation infrastructure.

However, it is the third indicator, Emergy Yield

Ratio, that is really interesting. This ratio is a measure of how agriculturally productive the system is; it measures 10 See the Appendix for an emergy analysis of the glaciers and a description of their importance for the regional ecosystems.






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how much emergy is yielded from the system for each unit of external emergy spent in making it productive. Values higher than one imply that one is gaining emergy by investing resources in it. As can be seen, on neither of them is this ratio higher than one, meaning that for the larger system of which the Callej6n forms part (i.e., Peri), and onto which these yields flow, there is a net loss of emergy for each unit that is spent there. I interpret these results as implying that much of the agricultural production on both subsystems is subsistence oriented and that in order to make them profitable in eme-rgy terms (not necessarily in economic, monetary ones) either less dependence on imported emergy sources would be needed, or a more local-labor intensive mode of production is to be adopted. This interpretation is consistent with the well known fact that highland agricultural systems are only marginally productive in an ecological sense, and that when maximization is sought, investment there makes no sense for individuals outside of the system (Gonzdlez de Olarte 1994:61-63; Zimmerer 1996:26-28). Something that implies that the economic development of these regions, and the economic betterment of the peoples who make them their homes, is indeed a difficult task. The reason is simple enough: one loses emergy by spending it there.






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This general observation, however, needs to be

understood as a broad generalization within which there is a lot of individual variability. The 0.57 and 0.68 emergy yield ratios for the Cordillera Blanca and Negra, respectively, are only subregional averages for which we cannot provide standard deviations. In Tumpa alone, for instance, one finds households with external emergy yields of only 0.03 as well as ones with yields of 2.41, with the community's average being 0.83 ( 0.10). This means that some households in the community--32.4% of them--are sources of net emergy gain to the larger system. If similar measurements were available for the region as a whole, one could develop emergy investment strategies that maximized emergy benefits. Whatever this strategy looked like, I believe it would also be economically beneficial to the local population as a whole since at least from my data I estimate (p = 0.0001) that a 1% increase in external emergy yielded would result in a 63.7% ( 14.5%) increase in their monetary income.

Finally, the last indicator on Table 7-1--Demographic Density--shows that the Cordillera Blanca is more densely populated than the Cordillera Negra. It is easy so see why this is so, given the above discussion of the other indicators. Resources there are more abundant making life






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easier. This does not mean, however, that there is more economic affluence on the Cordillera Blanca; If one looks at the average economic energy per capita on both systems as an estimate for economic affluence, one sees that it is very similar, and that it might be slightly higher in the Cordillera Negra given the need there to import more resources from outside. It is easier in the sense that in an overall manner there are more resources there to be employed in their productive activities. I consider these observations as highly suggestive given the theoretical framework of my research. Given the methodological possibility of separating economic and ecological resources from an commonly indiscernible pool of resources, but still measuring them along the same scale, one sees here that rural people tend to gather were ecological resources are more densely found. Not having the empirical data, I would however, expect to find that permanent out-migration to coastal areas is higher for communities on the Cordillera Negra than it is for those on the Cordillera Blanca.





Life in Tumpa; Seeking Development


During my first visit to Tumpa some 4 years ago, on

the summer of 1996 when scouting for a specific field site






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to carry out my research, I had the following first impression which I then wrote on my notes:


Tumpa, as a community, is notoriously more affluent
than the other communities I have visited... It has a
large central plaza with its own church and benches with green areas around them. The roads around the plaza are paved, and many of the main roads in the community not only have marked names, but are also
illuminated. There is a larger number of houses constructed with "noble materials" [concrete and
brick, instead of the more traditional adobe, referred
to as "rustic material"], and many of them have T.V.
antennas on their roofs.


After spending nine months there some of these first impressions stand, while others have necessarily changed. I would still say that in comparison to some other communities that I visited on the Callej6n, Tumpa is relatively more affluent. I would, however, add the cravat that this affluence is not necessarily shared by all of its members equally. Internal socioeconomic differences are quite marked, especially between community members who have started a small family businesses, such as stores in the community for selling medicines, foods and other small household items, and those members who dedicate most of their time to agricultural tasks. The average annual household monetary income in dollars is $546.8 ( 65.8), with the lowest household having an income of only $20 and the highest one of $1,591 dollars.






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Furthermore, the more affluent individuals are more

likely to be--or have been at some time--elected community authorities themselves, or at least are closely associated by others as being "more important" in community affairs in general. And as will be discussed below, community authorities also have access to community funds that they sometimes employ for their own purposes. Thus, contrary to my first impression of the community as being homogeneously more affluent, after the time spent there, internal social and economic differences become more apparent.

It would be difficult, however, to know whether these differences affect the affluence of community members when compared to other communities. In general terms, the more affluent individuals in Tumpa do seem to be better off than the more affluent members of neighboring communities, something that Tumpeftos themselves--in general--are very proud to emphasize to visiting Peruvian authorities and the occasional visitor like myself". But if this also means that the average tumpefto is better off economically than an average member of other close-by communities, I would not be able to make such a clear-cut statement. Nevertheless, 11Being on the foot hills of El Huascaran, a popular tourist attraction for hikers and high-mountain climbers, people in the region in general are accustomed to see "gringos" wondering around, at times






93


what I believe is important in this respect is the fact that all Tumpeftos consider themselves "more developed." They use, and have used this "more developed" ideology for at least fifty years, to argue that it should be there that a new district capital should be established.

Tumpa was, according to community documents, organized as a community in 1715 through the purchasing of lands by Don Francisco Guerrero de Almagro. He was a Spanish visiting judge who, via a royal decree, acquired the lands for his own use, employing what then were 17 families as peons for his hacienda. The community's name comes from the quechua term tumpanaqu:, meaning "the jealous ones," for according to the founding myth the original settlers when asked by Don Francisco Guerreo about their customs said they where very jealous of their wives, and closely prevented any extramarital sexual activities that might arise.

It was not, however, until 1966 that Tumpa was legally recognized as an "indigenous community" under the 1935 constitution. This is 3 years prior to president Velasco' s 1969 agrarian reform (see Hunefeldt 1995); implying that the inhabitants of Tumpa were free--at least legally--of renting small rooms To them (especially during the high tourist season).




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