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 Title Page
 Abstract
 Introduction
 Background
 Ethnographic linear programmin...
 Testing alternatives in a livelihood...
 Conclusions
 Reference






Group Title: Staff paper - University of Florida. Food and Resource Economics Dept. - SP 03-5
Title: Modeling diverse livlihood strategies in rural livlihood systems using ethnographic linear programming
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Permanent Link: http://ufdc.ufl.edu/UF00053828/00001
 Material Information
Title: Modeling diverse livlihood strategies in rural livlihood systems using ethnographic linear programming
Series Title: Staff paper
Physical Description: 14 p. : ill. ; 28 cm.
Language: English
Creator: Hildbrand, Peter E
University of Florida -- Food and Resource Economics Dept
Publisher: University of Florida, Institute of Food and Agricultural Sciences, Food and Resource Economics Department
Place of Publication: Gainesville Fla
Publication Date: 2003
 Subjects
Subject: Ethnology -- Methodology   ( lcsh )
Ethnology -- Mathematical models   ( lcsh )
Farm families -- Demographic surveys   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 12-14).
General Note: Cover title.
General Note: "October 2003."
Funding: Florida Historical Agriculture and Rural Life
Statement of Responsibility: by Peter E. Hildebrand ... et al.
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Bibliographic ID: UF00053828
Volume ID: VID00001
Source Institution: Marston Science Library, George A. Smathers Libraries, University of Florida
Holding Location: Florida Agricultural Experiment Station, Florida Cooperative Extension Service, Florida Department of Agriculture and Consumer Services, and the Engineering and Industrial Experiment Station; Institute for Food and Agricultural Services (IFAS), University of Florida
Rights Management: All rights reserved, Board of Trustees of the University of Florida
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oclc - 53246258
notis - APM4937

Table of Contents
    Title Page
        Title Page
    Abstract
        Page 1
    Introduction
        Page 2
    Background
        Page 3
    Ethnographic linear programming
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
    Testing alternatives in a livelihood system and aggregating with diversity
        Page 11
    Conclusions
        Page 12
    Reference
        Page 12
        Page 13
        Page 14
Full Text


SP 03-5


STAFF PAPER SERIES


MODELING DIVERSE LIVELIHOOD STRATEGIES IN RURAL
LIVELIHOOD SYSTEMS USING ETHNOGRAPHIC
LINEAR PROGRAMMING

by
Peter E. Hildebrand, Norman E. Breuer, Victor E. Cabrera, and Amy J. Sullivan


Staff Paper SP 03-5


October 2003


~F UNIVERSITY OF
S**FLORIDA
Institute of Food and Agricultural Sciences
Food and Resource Economics Department
Gainesville, Florida 32611










MODELING DIVERSE LIVELIHOOD STRATEGIES IN
RURAL LIVELIHOOD SYSTEMS USING
ETHNOGRAPHIC LINEAR PROGRAMMING
Peter E. Hildebrand1*, Norman E. Breuer2, Victor E. Cabrera3, and Amy J.
Sullivan3

'Professor, Food and Resource Economics, University of Florida, 2126 McCarty Hall, PO Box
110240, Gainesville FL 32611-0240
2Research Scientist, Rosenstiel School of Marine and Atmospheric Science, University of Miami,
4600 Rickenbacker Causeway, Miami FL 33149-1098
3PhD Candidate in Interdisciplinary Ecology, School of Natural Resources and Environment,
University of Florida, 2126 McCarty Hall, PO Box 110240, Gainesville FL 32611-0240

Abstract

Although world population is becoming increasingly urbanized in percentage terms, absolute
numbers of small-scale, resource-limited farm families continue to rise. Development efforts
must continue to focus on this numerous and vulnerable sector. Strategies for survival of
individual households in these livelihood systems are highly diverse. Adding to this diversity is
that these households are first homes, not businesses, so goals are different from those commonly
used in economic analyses. Because of the diversity and their limited resource base, the use of
averages seriously overestimates aggregated potential response. In this article, we present a
methodology, Ethnographic Linear Programming (ELP), designed specifically to aid in
understanding and examining potential improvements for diverse rural livelihood systems.
Ethnographic methodologies for data collection reduce or eliminate the need for making
assumptions. For this reason, different household characteristics, primarily household
composition, found in the livelihood system are built into models) so that results more accurately
reflect the diversity found in the livelihood system. Conclusions are based on the differential
response of households to the different alternatives being tested rather than extrapolating from
averages. ELP is a cost-effective way of understanding varied household responses to potential
changes such as introduced production technologies, infrastructure availability, and governmental
policies, as well as to shocks. ELPs are constructed on standard computer spreadsheets and are
user friendly. Although other objective functions may be appropriate, we have found that one of
the most useful is maximization of discretionary cash subject to household food security and
other minimum or maximum constraints. Household composition, seasonality of activities and
gender issues of access to and control of resources are important considerations. Coefficients for
these are elicited using participatory methods. Dependable yields reported by farmers are used
rather than averages or expected yields. ELP is a useful tool for agricultural researchers and
technology developers, policy makers, and managers of infrastructure and natural resources to
help them understand the varied potential responses of diverse households to proposed
modifications. ELP is a dynamic, adaptive methodology that has evolved through an iterative
trial and error process. It will continue to change but is sufficiently robust to be more broadly
disseminated and used. The methodology is a working tool applicable to ex ante evaluation of
proposed changes and has been used by researchers in a number of countries and for several
purposes.

* Corresponding author. Tel.: 1-352-392-5830 ext 436; fax: 1-352-392-8634. E-mail address: peh@ufl.edu










1. Introduction


Individual households in peasant livelihood systems are highly variable and not well
suited to being averaged into typical or representative cases such as often used in
analyzing them in development studies. Adding to this diversity is that these households
are first homes, not businesses, so goals are different from those commonly used in
economic analyses. Economic analyses of business enterprises consider that all resources
contribute to the production of the business product or products and the basic objective of
the firm is profit maximization. Because the peasant household is first a home, there are
many more objectives to be considered than just that of "profit." Further, in peasant
households, a significant portion of household resources are consumed in reproduction
activities, so are not available for production activities. Reproduction activities are those
involving the maintenance of the home, the household and its members. Included in most
households are such tasks as food preparation, child care, washing clothes, fetching water
and firewood, collecting wild plants for food or medicine, and tending small animals and
home gardens. These tasks are often, but not exclusively those of women and children,
and time required will vary with household composition. For this reason, these activities
must be explicitly accounted for in household models.

In our context, a livelihood system is considered to be the composite of all activities
available to all households in the system from which to choose to secure their livelihoods.
Livelihood systems are not synonymous with communities or regions. Different
households within a community may have available different activities for reasons of
wealth, religion or caste. It is important to recognize these differences and to create
models that are livelihood-system specific. The activities that an individual household
selects from among those available in its livelihood system are the livelihood strategies
of that household and are household specific. Within a livelihood system, households
with similar composition would be expected to have similar livelihood strategies.

Because most people who undertake economic analyses are not from peasant households,
the first function of modeling these systems is to understand 1) what is done, 2) who does
what, 3) when it is done, 4) why it is done, and 5) how it is done. Assumptions,
commonly used in economic modeling to substitute for missing data (or knowledge)
(Just, 2002) inevitably lead to erroneous solutions and conclusions because the
assumptions are based on an inadequate understanding of the system being modeled. In
ethnographic linear programming (ELP), ethnographic methodologies for data collection
reduce or eliminate the need for making assumptions. When models do not conform to
what is being observed in the field, the modeler works with the households being
modeled to ascertain what has created the unrealistic results, and adjustments are made
based on the new ethnographic data (knowledge) rather than on assumptions, which often
artificially adjust the model so results conform to preconceived concepts of what the
system should be.

With the wide availability of laptop computers, modelers can take the models to the field
to validate and calibrate the models directly with the subjects involved. Once the model









or models are calibrated and validated, that is, they reflect the reality found in the field,
they can be used for testing alternatives such as improved infrastructure, different
policies or new technologies. But because of the diversity among peasant households and
their limited resource base, the use of averages seriously overestimates aggregated
potential response. For this reason, different household compositions found in the
livelihood system are built into the models) so that results more accurately reflect the
diversity found in the livelihood system. Conclusions, then, will be based on the
differential response of households to the different alternatives being tested rather than
extrapolating from averages.

2. Background

A linear program (LP) is a mathematical "optimizing" procedure that has been used more
than half a century (Dorfman, 1951) to maximize or minimize an objective, subject to a
set of constraints. LP models have been used extensively to formulate farm plans to
search for the optimal solution to a problem of allocating constrained resources-
typically land, labor, and capital-to various alternative means of production.

Linear programming mathematically can be stated as:

Max (or Min): IH = CjCjXj (j= 1 ... n)
Subject to: ZiAijXj <= Ri (i= 1 ... m)
And Xi >= 0

17 is the variable objective to be minimized or maximized, Cj is the cost (debit) or returns
(credit) of each of the n activities Xj Aij is the set of input or output coefficients for each
activity j and resource or constraint i, and Ri is the set of m minimum or maximum
constraints or restrictions.

When agricultural economists began using LP models in the 1950s (Heady 1958) they
used them to model farms to show farm managers how they could better allocate their
resources and thereby increase profit. These models were normative in nature; the
modelers anticipated telling farmers what they "ought to do" when the solutions to the
models differed from what the farmers "were" doing, which was usually the case.

As it concerns household livelihood decision-making, linear programming is a basic tool
for economic analysis of small farm livelihood systems (Hildebrand and Sullivan 2002).
In farming systems LP models, the optimal solution often involves a combination of
goals such as maximizing cash available for discretionary spending after meeting other
household goals which may include food security, education expenses, etc. (Bernet et al.
2001, Hazell and Norton 1986, Pannel 1997). By producing an LP model that represents
a real-world livelihood system, it is then possible to run relatively inexpensive
assessments of both policy scenarios and project ideas generated by extension agencies,
infrastructure managers and policy-makers (Bernet et al. 2001, p.184). In this way
researchers and extension agents can test production alternatives in the virtual livelihood
system of the linear programming model. This will provide an estimate of how the real









world might respond-as seen through a change in resource allocation decisions
(livelihood strategies) (Hazell and Norton 1986, Pannel 1997). This is done by adding
alternatives to existing crop and input activities to assess the likely reaction of farmers
when presented with new technologies, markets or shocks (Mudhara 2002). Thangata
(2002), working in Malawi, studied labor scarcity with regards to improved fallow
adoption. The novel element of his work was the incorporation of the effects of
HIV/AIDS on labor availability and agroforestry adoption.

3. Ethnographic Linear Programming

3.1 Introduction

Because a livelihood system is the composite of all activities available to households in
the system from which to choose to secure their livelihoods, it is the livelihood system
that forms the basic matrix of the ELP. This facilitates data gathering because the basic
matrix is common to all the households in the system.

3.2 Basic matrix
3.2.1 All available on-farm and off-farm production activities
Crop and livestock production activities conducted on the farm are included in this
category as are activities off the farm such as hiring out labor, working for another entity,
production of remittances, etc.
3.2.2 All common reproduction activities
Reproduction activities are those that contribute to the maintenance of the household and
its members.
3.2.3 All available resources (land, labor, water, bush, cash) and constraints to be
met (food, cash, other household goals)

Most limited resource farm households face similar kinds of constraints (land area,
household labor availability, hired labor availability, consumption requirements, cash
needs, markets, water availability if they have irrigation), but the degree that these
constraints are binding on household options vary with the specific circumstances of each
household. Some (water, markets, hired labor, and sometimes land) are exogenous to the
household and are fixed in quantity, quality and/or capacity. Others are endogenous to
the household (household labor, consumption requirements, cash needs, and sometimes
land) and vary over time with household composition.

The main goal of most limited resource farm households is to meet minimum household
consumption requirements (including food, clothing and shelter) in an endeavor to
reproduce the family unit. Sometimes education for the children is included. Beyond the
necessities of life, the farmers may wish to maximize discretionary cash for purchase of
items that are desirable but not necessities. Other goals of a household to be maximized
or minimized for various reasons may be: minimize male labor, maximize the production
of a particular crop, etc. These goals can be included in the model as fixed minimum or
maximum constraints or one at a time can be incorporated in the objective function to be
minimized or maximized.










Seasonality or periodicity is important in small farm livelihood systems. Labor and cash
resources should be considered at least by semester. Smaller divisions such as month or
week may be necessary in some cases. Labor and cash need to be disaggregated into at
least two semesters and for different types of labor. Expenses are incurred at the
beginning of the respective period, and income is generated at the end of the period.

3.2.4 Common coefficients used in the matrix (amount of resources used or product
derived from each production and reproduction activity)

Input and output coefficients (ais) for each of the production and reproduction
activities will generally be quite similar within a given livelihood system and
usually can be obtained using focus groups or in-depth interviews of farmers.
Information is needed on inputs required, source, cost if any, and time of
acquisition. In ELP we use coefficients consistently reported by farmers. The
amount of time required to prepare, plant or weed an acre or hectare of a crop or
care for animals and who in the household is involved (is it a man's job, a
woman's job, a child's job) will be similar, particularly in households with similar
household composition. Time and other resources required for reproduction
activities such as food preparation, washing clothes, tending children, etc. are also
similar. However, the same operation in production activities may be different for
different kinds of soils, types of households, ethnic groups, etc., and should be
recognized. Also, coefficients for reproduction activity often vary with household
composition and must be modified for each household. ELP is flexible enough to
account for variation in farmer practices by incorporating different practices as
separate activities. For example, if different kinds of soil are managed differently
and produce different yields, each should be a separate activity.

Data should be obtained separately for each perennial crop or crop association the
household pursues and on a per unit of land area basis or per plant basis, if appropriate
(for instance, in the case of some trees). Numbers needed are the same as for annual
crops. Additionally, number of years before the crop produces is important information.

The same data needed for annual crops are required for each kind of animal activity the
household pursues and on a per animal basis. Additionally, data for death loss, birth rate,
weaning rate, etc., must be gathered.

All data elicited for other activities (annual and perennial crops, animals) should also be
gathered with regard to forest, bush or body of water. These might include extraction of
firewood, medicinal plants, or food stuffs in time of shortage, etc. Ownership
(community, ejido, tribe, other) should be explored, as well as traditional or locally
accepted standards for use of these resources.

Particular attention must be paid to seasonality of strategies within each livelihood
system. Researchers can expect fluctuation of everything from consumption to labor
availability throughout the year. In addition to understanding what fluctuates, the









temporal dimension of these changes must be recognized; for example a hungry season,
months when school is in session, time when men migrate to find work, etc. This
seasonality can and must be incorporated into to the model to ensure adequate simulation.
Essentially all aspects of the model are subject to seasonal fluctuation. Recognizing and
accommodating this in the model is crucial to ensuring adequate simulation of livelihood
systems.

Yields that are dependable or can be "counted on": implicit risk.
It is necessary to know and include in the model the amount produced of each usable
product, when it is available, how it is used, and who has control of and access to each
usable product to determine 'whose' account receives benefits.

One of the most difficult things to ascertain, yet crucial for simulating livelihood systems,
is the 'dependable' yield, or yield that farmers 'count on' for their basic food crops. It is
conventional to ascertain and use "usual" or "average" yields when analyzing farm
systems. Even though this seems logical, it is not the measure of productivity that will
provide good descriptions of these households, nor the one used by farmers. Farmers
cannot depend on "average" yields; they know that often yields will be below "average."
For basic food crops, they calculate area to be planted based on a yield level that will
occur with greater frequency. They may, for example, want to plant a large enough area
so that they can feed the household 90% of the time, or nine years out often. The yield
level that meets this criterion will be much lower than average yields widely accepted in
economic analyses. This dependable lower yield level is what farmers can "count on"
most of the time (Figure 1). Because this is the yield level farmers use for planning how
much land to plant to their basic food crops, this is the level that must be used in the
ethnographic linear program model. Allan (1965) recognized this situation in what he
termed the production of a 'normal surplus' of food in the average year.







60


80 .....-----..


100
dep Yield
Ydep Y
Figure 1. Dependable (Ydep) versus average yield.

It is more difficult to obtain an estimate of dependable yield than of average yield. One
way found to be useful is first to find out how much of the basic food crop (or crops) the
household needs for a year's supply (for an annual crop) and then ask about how much










land is usually planted to this crop. Another would be to ask about how much land is
planted to the crop and then ask about how much product they need from this land most
of the time.

Prices
Prices of products sold are often taken from local markets, or worse, national data. For
farm products purchased at the farm gate, the price paid to the farmers at that point (if
commonly used by farmers in the livelihood system) must be used. If farmers take the
products to a road or waterway where buses, trucks or boats pass by then the price paid
the farmers at that point, or the charge for taking the farmer and his/her produce to a
market must be taken into account. The time required for marketing farm produce must
also be taken into account. This is not charged as a cash cost but rather as time used by
the relevant person or persons in the selling activity. Sometimes farmers are not paid at
the time of transaction, but must wait days or weeks to be paid. This affects seasonality
of cash flow so must be taken into consideration in the model. The additional labor for
returning to the source of the cash must also be considered.

Local market prices of items purchased by farmers are probably relevant, but cost of
getting some of these items to the farm must also be considered.

Credit
Costs of credit are complex. Farmers often pay very high interest for credit supplied by
local money lenders simply because of convenience or lack of other real alternatives.
Transaction costs for formal credit often include the labor costs of multiple visits to the
lending agency, first for purposes of formalizing the loan application, and second, to
return for the money if the application is approved. If a formal credit scheme is to be
considered, these costs must be taken into account as they can require cash (for bus fare,
for example) as well as lost time away from the farm.

Units
In talking with limited resource farmers, care must be taken to assure that all units are
completely understood. Local usage of weights (quintal, kg, lb., ton) or volume (bushel,
fanega, almudes, latas) must be conversed with the farmers in their own language.
Conversions can later be used (or not) to scientific notation units to be acceptable to
professional journals.

Labor
Labor required during each period, disaggregated by gender (usually sufficient: male and
female children, male and female adolescents, male and female adults), in terms of who
performs which activities. It is important to obtain data that reflect each step (land
preparation, seeding, weeding, harvest, etc.) in each activity. These data must account
for alternative types of households, such as female-headed or multi-generational, as well
as any activities undertaken by hired or exchange labor.

The concept of the eight-hour working day is uniquely Western and Northern. Local
peoples have different ways of describing and understanding the length of a working day.









It may vary depending on effort (toil) expended, length of walk to reach working place,
opportunities for gathering fruits vegetables, firewood or water on the way home, etc. A
day could be 4-10 hours long. This must be taken into account. A day's work may vary
depending on whether a person is working his or her own land rather than as a laborer for
another farmer. Food may or may not be included when wages are paid. Boys and girls
begin working in the fields or with livestock at different ages in different cultures. Time
spent on farm tasks usually varies when school is in session and when not (seasonality).
Likewise, children help with household chores. The age at which this begins, as well as
time spent daily is important to know. Observations, focus groups, informal interviews,
and participatory tools including games, are useful for obtaining as well as understanding
these data.

Land area
The amount of land a farmer owns as well as how much he or she farms is important to
know. Land in use may vary from one season or one year to another. Differential time
required for different tasks (on one or various fields) should be explored and incorporated
into the model. It is also quite common for farmers to rent fields, and this is relatively
easy to incorporate into models. Local rates, as well as if payment will be made in cash
or kind should be known. Time required to travel to distant fields must also be accounted
for.

The above provides the information for the basic input/output matrix of the
livelihood system, which will be common to households sharing the same
livelihood system.

Household-specific information (same activities unless misdiagnosed)

Constraints such as land area, labor availability, consumption requirements, cash
needs, and goals such as to meet food needs for the household, maximize
discretionary cash, minimize male labor, maximize production of a basic food
crop are highly diverse from farm to farm even in a relatively homogeneous area
and depend to a large extent on household composition. So after the information
for the basic matrix is collected, it is necessary to begin being specific.

Perhaps the most efficient means of constructing the ELP model after the basic
input/output coefficients have been estimated is to select a willing household whose
farming practices reflect those in the basic matrix and model that specific household.
The process is to obtain information from the household members individually and as a
group on all the relevant activities and constraints. This must be specific enough to
identify 1) who eats how much of each kind of food; 2) who can use cash from various
sources; 3) what are the necessary cash expenses for the household; 4) when during the
year must cash expenses be made, and who is responsible for each; 5) what each member
of the household does; and 6) how much time is spent doing it, on a monthly or other
relevant periodic basis. Some of the reproduction activities will take more or less time as
household composition varies. These coefficients in the basic matrix are variable as
household composition changes so they will need to be varied for different households.









It is important to note alternative types of households such as female-headed or'multi-
generational in terms of who performs which activities, when, and how. Seasonal cash
accounting is critical and must include remittances from persons who work off the farm
whether or not in residence. Frank discussions with the household members may be
necessary to elicit goals of individuals within the household.

Given the wide availability of laptop computers, the complete descriptive model
of a specific household can be constructed and modified as these discussions are
going on. Often inconsistencies can be spotted when the model is infeasible or
the solution is inconsistent with what is known or described.

3.2.5 Resource use and availabilityfor each production and reproduction activity

Interviews reveal the amount of land available for each kind of use (upland, lowland,
irrigated land, pasture, bush, fallow, orchard, forest, etc.), and who controls the land and
who has access to it. Land tenure should be explored as it could have important credit
and other implications.

When irrigation is an option, indications of the adequacy of water are necessary. Area of
land irrigated in season is one indication and would probably provide the most usable
coefficient for the matrix. But it is important to recognize that farmers probably are
spreading the water to the maximum amount of land rather than applying it at a rate to
maximize production per unit of land area.

Data should be obtained separately for each annual cropping activity or crop association
the household pursues, based on their measurements, to be converted later if necessary.

Consumption requirements

Seasonal cash needs are important to determine. These include information on: for what,
in what amounts and from what sources. These should include non-discretionary
amounts for such things as education, clothes, gifts, etc. Also the researcher needs to
know how much cash is needed throughout each season, for inputs, by each member of
the household and who is responsible for having it available. Off-farm sources of income
as well as remittances from persons not living in the domicile should be included.

Data on socially acceptable or usual food consumption (type and amount of food) of
persons is needed, as often ELPs are modeling households subject to food security.
Information should include seasonal differences. Sources of the food-whether
purchased, hunted, gathered, or produced-and usual amounts from each source are
required. Special foods for infants and young children, if any, should be recorded.

3.2.6 Household goals (maximum or minimum) and objective functions)

A limited resource farm household will have many goals and objectives, and it may be
difficult for farmers to elicit them, much less put them in order of priority. Also,









different household members may have different goals as well as varying capabilities in
realizing them. Some goals may be unintentionally overstated. For example, minimum
food requirements or minimum cash needs may reflect desire rather than need. Use of
overstated goals as constraints in the models can result in infeasibilities. These will need
to be addressed and adjusted during the calibration and validation process which can start
with the first household being modeled.

3.2.7 Infeasibilities in the model

Infeasibilities (no solution possible) can be the result of any number of problems in
model specification. Perhaps the most common are the overstatement of food and cash
needs that reflect wants rather than needs. If the food and cash needs are set at unrealistic
levels then infeasibilities can easily occur, or else the solution does not reflect the
livelihood strategies of the household being modeled. When this occurs, limitations
within the system such as minimum cash required and/or food consumption can be
reduced iteratively to see which is more constraining. In the case of cash, once the model
returns a feasible solution, it indicates that the particular household is living with less
cash than reported. In the case of food, if the model gives an infeasible solution, it means
that particular household is eating less than commonly understood or reported. Gough
(2002) in a study conducted in Malawi, found that although many individual household
models were infeasible with reported consumption levels, when the model was scaled up
to the community level, there was enough food for all. These results may be a good
indication of the existence of local safety nets, sharing, and long-term exchange at the
village level.

3.3 Calibration and validation with other households in the livelihood system (using
Visual Basic)

When the ELP model adequately describes the first household, the model can be run with
data from another household as part of the process of simultaneous calibration and
validation. Considerably less time will be required in interviewing subsequent
households than was required for the first. Note that the term "adequately" is subjective.
The model should reflect the correct strategies (that is, the correct activities of the
modeled household). The magnitude of each activity should be relatively close to what
the actual household does but it should not be expected that they would be exact. Exactly
meeting the magnitude often means that an artificial constraint has been built into the
model. If that was necessary for the model to describe the household then one or more
constraints are usually missing or the magnitude of an input/output coefficient is
incorrect. More discussions with the members of the household will be required.

The composition, requirements and constraints of this second household should be
different from the first, but share the same livelihood system. If the first ELP adequately
reflects the livelihood system being modeled, changes in the subject household should
only require changing the household composition. This, of course would change
household consumption requirements, cash needs and labor availability as well as the
variable coefficients in some of the reproduction activities. Land area may need to be









changed if the second household is larger and has more labor resources than the' first.
The solution to this ELP model should be close to what this second household actually
does. This is normally a subjective call when working in the field.

A limited number of additional households of diverse compositions should also be
modeled in this process of calibration and validation. The model'can be considered
validated when it adequately simulates or describes each of these diverse households.
This type of participatory calibration and validation has been carried out by several
researchers in Zimbabwe, Malawi and Ecuador with good results.

Building an applied model is a process, and the most successful models evolve through
time to take into account new findings. There never is a definitive version, but rather at
any moment in time the model represents a kind of orderly data bank that reflects both
the strengths and limitations of the available quantitative information. Through
validating the model, information is obtained about its structure. While judgments on the
model's adequacy must be made, it also is important to continue to improve the model

In the case of data reconciliation, it may not be obvious which parameters are the causes
of inaccuracy. Crop outputs that are higher than reality may be caused by (1)
overestimates of yields or crop prices for products sold, (2) underestimates of input
requirements, (3) underestimates of input prices, (4) yield and input errors for competing
crops, or (5) overestimates of resource availability. The last factor, however, is likely to
be the cause only if there is a systematic overstatement of production across all products.
Each of these factors may be worth investigating.

Each adjustment and the reasons behind it must be documented. Arbitrary adjustments in
parameter values, with the aim of improving the model's fit, should be strictly avoided.
The initial version of the model has to be documented, and then each subsequent change
should be recorded along with the reason for the change. It is far preferable to have an
unsatisfactory fit, but a clear documentation of the model's development, than a better fit
but arbitrary parameter values. Among other things, a model with arbitrary adjustments
does not provide a basis for future work and extensions. This aspect of the work is
stressed because it is an area where slips frequently occur with large models if only
because the model builders are under time pressure. Rules for altering the model after the
initial validation attempt should be (1) to change the model only if further investigation
yields new information, and (2) to document all changes. If done in this spirit, the
validation process can lead to a better model.

4. Testing alternatives in a livelihood system and aggregating with diversity

Once the model is calibrated, it can be used for hypothesis testing or pre-evaluation of
alternative technologies, activities, infrastructure or policies. The new option under
consideration is added to the matrix so that the ELP solves for the desired objective
function when the proposed new option is in competition with existing farm activities.
Governments often make policy decisions without taking into account how they may
affect different sorts of farm households. For example, a decision could be made to









import fertilizer, and credit for farmers could be tied to its use. However, the application
of fertilizer would require additional days of labor. In this case, a column for the new
activity is added to the matrix (fertilized maize, for example), without eliminating the
type of maize production already present in the model. The model can then be solved for
a number of households with different compositions to estimate the impact on different
kinds of households. Aggregating on the basis of the relative frequency of these types of
households in the livelihood system provides a much better estimate of potential effect
than if only an "average" household were aggregated for this purpose.

5. Conclusions

Ethnographic linear programming is a useful tool for agricultural researchers and
technology developers, policy makers, and managers of infrastructure and natural
resources. It can help them understand the varied responses of diverse households to
potential modifications. ELP is a dynamic, adaptive methodology that has evolved
through an iterative trial and error process. It will continue to change but is sufficiently
robust to be more broadly disseminated and used. The methodology is a working tool
applicable before and during projects, rather than as an analysis tool for obtaining static
results after the fact.

In its developing stages ELP has been used for a number of applications by researchers in
the areas of rural development and natural resource management, and it is amenable to
other uses. For example: Cabrera (1999) assessed the potential adoption of asparagus
among small farmers in Periu; Kaya et al. (2000) assessed adoption of improved fallows
in Mali; Litow (2000) assessed the potential impact of milpa production by small farmers
on the forest in the Maya Biosphere Reserve in Guatemala; Breuer (2000) assessed the
potential for medicinal plant production in Paraguay; Bastidas (2001) assessed the impact
on small farmers of potential changes in irrigation water availability in the Andes of
Ecuador; Mudhara (2002) and Thangata (2002) assessed the potential for improved
fallow adoption in Zimbabwe and agroforestry in Malawi, respectively; and Breuer(2003)
assessed sustainability, food security and improved worker livelihoods in an Ecuadorian
agrosocioecosystem dominated by banana plantations.

REFERENCES

Allan, W. 1965. The normal surplus of subsistence agriculture. Chap 4 In: Allan, W.
The African husbandman. Oliver and Boyd, London and Edinburgh.

Bade, J., Hengsdijk, H., Kruseman, A., Kuyvenhoven, A., Ruben R., 1996. The effect of
agrarian policy on farm household, welfare, and sustainable land use: a modeling
exercise. In: Proceedings of the 14th International Symposium on Sustainable
Farming Systems 11-16 November 1996 Colombo, Sri Lanka, pp. 393-402.

Bastidas, E.P. 2001. Assessing potential response to changes in the livelihood system of
diverse, limited-resource farm households in Carchi, Ecuador : modeling









import fertilizer, and credit for farmers could be tied to its use. However, the application
of fertilizer would require additional days of labor. In this case, a column for the new
activity is added to the matrix (fertilized maize, for example), without eliminating the
type of maize production already present in the model. The model can then be solved for
a number of households with different compositions to estimate the impact on different
kinds of households. Aggregating on the basis of the relative frequency of these types of
households in the livelihood system provides a much better estimate of potential effect
than if only an "average" household were aggregated for this purpose.

5. Conclusions

Ethnographic linear programming is a useful tool for agricultural researchers and
technology developers, policy makers, and managers of infrastructure and natural
resources. It can help them understand the varied responses of diverse households to
potential modifications. ELP is a dynamic, adaptive methodology that has evolved
through an iterative trial and error process. It will continue to change but is sufficiently
robust to be more broadly disseminated and used. The methodology is a working tool
applicable before and during projects, rather than as an analysis tool for obtaining static
results after the fact.

In its developing stages ELP has been used for a number of applications by researchers in
the areas of rural development and natural resource management, and it is amenable to
other uses. For example: Cabrera (1999) assessed the potential adoption of asparagus
among small farmers in Periu; Kaya et al. (2000) assessed adoption of improved fallows
in Mali; Litow (2000) assessed the potential impact of milpa production by small farmers
on the forest in the Maya Biosphere Reserve in Guatemala; Breuer (2000) assessed the
potential for medicinal plant production in Paraguay; Bastidas (2001) assessed the impact
on small farmers of potential changes in irrigation water availability in the Andes of
Ecuador; Mudhara (2002) and Thangata (2002) assessed the potential for improved
fallow adoption in Zimbabwe and agroforestry in Malawi, respectively; and Breuer(2003)
assessed sustainability, food security and improved worker livelihoods in an Ecuadorian
agrosocioecosystem dominated by banana plantations.

REFERENCES

Allan, W. 1965. The normal surplus of subsistence agriculture. Chap 4 In: Allan, W.
The African husbandman. Oliver and Boyd, London and Edinburgh.

Bade, J., Hengsdijk, H., Kruseman, A., Kuyvenhoven, A., Ruben R., 1996. The effect of
agrarian policy on farm household, welfare, and sustainable land use: a modeling
exercise. In: Proceedings of the 14th International Symposium on Sustainable
Farming Systems 11-16 November 1996 Colombo, Sri Lanka, pp. 393-402.

Bastidas, E.P. 2001. Assessing potential response to changes in the livelihood system of
diverse, limited-resource farm households in Carchi, Ecuador : modeling









livelihood strategies using participatory methods and linear programming. PhD
dissertation, University of Florida, Gainesville.

Bernet T.O., Ortiz, O., Estrada, R.D., Quiroz, R., Swinton, S.M., 2001. Tailoring
agricultural extension to different production contexts: a user-friendly farm
household niodel to improve decision-making for participatory research.
Agricultural Systems 69, 183-198.

Breuer, N.E. 2000. The role of medicinal plants in rural Paraguayan livelihoods. M.A.
thesis, University of Florida, Gainesville.

Breuer, N.E. 2003. Linking sustainability, food security and improved worker
livelihoods in an Ecuadorian agrosocioecosystem. PhD dissertation, University of
Florida, Gainesville.

Cabrera, V.E. 1999. Farm problems, solutions, and extension programs for small
farmers in Cafiete, Lima, Peru. M.S. thesis, University of Florida, Gainesville.

Dorfman, R. 1951. Application of linear programming to the theory of the firm.
University of California Press, Berkeley.

Gough, A.E. 2002. The starter pack program in Malawi: implications for household
food security. M.S. thesis, University of Florida, Gainesville.

Hazell, P.B., Norton, R.D., 1986. Mathematical programming for economic analysis in
agriculture. New York, Macmillan.

Heady, E.O. 1958. Linear programming methods. Iowa State College Press, Ames.

Hildebrand, P.E., Sullivan, A.J., 2002. Small farm livelihood systems and food
security: addressing diversity. Staff Paper Series SP02-7. Food and Resources
Economics Department, IFAS, University of Florida, Gainesville, FL, USA.

Just, R. 2002. An overview of public information and the agriculture and food system.
Available at http://www.cfare.org/publications/CFARE2003Symposium.pdf

Kaya, B., Hildebrand, P.E., Nair, P.K.R., 2000. Modeling changes in farming systems
with the adoption of improved fallows in southern Mali. Agricultural Systems 66:
51-68.

Langholtz, H.J., Marty, A.T., Ball, C.T., Nolan, E.C., 2001. Resource-allocation
behavior. Kluwer Academic Publishers, Boston.

Litow, P.A. 2000. Food security and household livelihood strategies in the Maya
Biosphere Reserve: the importance of milpa in the community of Uaxactfin,
Guatemala. M.S. thesis, University of Florida, Gainesville.










Mudhara, M. 2002. Assessing the livelihood system of smallholder farm households:
potential for adoption of improved fallows and green manure in Zimbabwe. PhD
dissertation, University of Florida, Gainesville.

Pannel, D., 1997. Introduction to practical linear programming. John Wiley and Sons,
New York.

Sicular, T., 1986. Using a farm household model to analyze labor allocation on a
Chinese collective farm. In: Singh, I, Squire, L., Strauss, J. (Eds.), Agricultural
household models: extension applications and policy, pp 277-305. The Johns
Hopkins University Press, Baltimore, MD.

Thangata, P.H. 2002. The potential for agroforestry adoption and carbon sequestration
in smallholder agroecosystems of Malawi: an ethnographic linear programming
approach. PhD dissertation, University of Florida, Gainesville.




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