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FARMING SYSTEMS AND FARMER-DRIVEN PROBLEM SOLVING
Robert Zabawa and Christina Gladwin
Paper presented at the Conference on Domestic Farming Systems sponsored by the
Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences,
University of Florida, Gainesville. September 10-13, 1984.
*Robert Zabas~e has a Ph.D. in Anthropology and is a Research Assistant in
the Food and Resource Economics Department, University of Florida. Christina
Gladwin is an Assistant Professor in the Food and Resource Economics Department,
University of Florida. The research was supported by a National Science Founda-
tion grant awarded to Christina Gladwin. The Co-authors are grateful to NSF,
the farmers of Gadsden County, and John Russell, Extension Director. The content
of this paper is solely the responsibility of the authors.
~FARMING SYkSTEMS ND FARMEi-DRIVEN~ PROBLEM SOLVING
ROBERET ZABAWA AND CHRIiSTINIA GLAD~WIN
Farming systems research and extension programs are `now
generally viewed as having some hope of increasing food
production on small rainfed farms in the Third World
(Gilbert et al. 1980; Shaner et al. 1981). Recently, the
farming systems approach has also been effectively tried on
small-scale family farms in the United States as well, as
the participants in this conference have shown. Approaches
to farming systems programs are varied, with debates raging
about "downstream" versus "upstream" approaches, and FSIP
versus FSR/E (the farming systems' approach to
infrastructural support and policy' versus its approach to
technology generation, evaluation, and delivery> (Norman and
Gilbert 1981; Norman 1982).
In general, however, all farming systems programs share:
A concern with small-scale family farmers wJho
generally reap a disproportionately small share of
the benefits of organized research, extension, and
other developmental activities;
A recognition that a thorough understanding of the
farmers' situation is critical to increasing their
productivity and to forming a basis for improving
their welfare; and
The use of scientists and technicians from more
than one discipline as a means of understanding
the farm as an entire system rather than the
isolation of components within the system
rHildebrand and Waugh 1983:4).
The focus of a farming systems project is the farmer,
rather than the crop, the technologyJ, or the environment
(CIMMYT Economics Program 1980). The farming systems
approach thus starts with the farmers' constraints and
develops, through experiments on their fields-,
recommendations to improve their family' s standard `of
living. Most farming systems programs accomplish this aim .
via a multidisciplinary team that, first, diagnoses farmers'
problems, goals, and constraints; second, identifies new
technologies or strategies to deal with or alleviate those
constraints; third, tests the promising technologies or
strategies via experimentation and on-farm tests; and
fourth, diffuses or extends the new technologies or
strategies to the local farmers (Gilbert et al. 1980).
A9s farm trials and farmers' tests are on farmers'
fields,. and the farmer is' consulted dur ing both the
diagnostic and the evaluation stages, the farmer is clearly
at the center of the program and farming-systems projects
all espouse the goal of involving farmers more explicitly at
each stage (of diagnosis, technology development, and
technology assessment). In attempting to reach this goal it
is vital, therefore, that the farming systems program should
"...know what decisions the farmer is making, what
alternatives he is considering in each decision context, and
why he chooses a particular outcome" (Gladwin 1983:148).
This perspective then, necessarily adds a new dimension to
the farming systems project. Not only is the farmer the
focus of the research, but he or she is also the main driver
of the research. Farming systems research is not only
directed toward the farmer; the farmer, in a manner,
directs the research as well.
One approach to this goal is the "ethnoscientific"
approach that concentrates on the cultural symbols used by
the farmers. The aim is "to grasp the native's point of
view, his relation to life, to realize his vision of his
world" (Malinowski 1922:25). To see the insider's world
through the insider's eyes is the goal of ethnography, which
differs from other social sciences in its emphasis on
indigenous folk knowledge rather than on scientific
knowledge. Because "the subject matter in ethnoscience is
not environmental phenomena as such, but people's knowledge
and interpretation of these phenomena"(Glick 1964:273), an
ethnoscientific approach of involving farmers in farming
systems research is quite different from previous
approaches. It differs most notably in use of trained
personnel and choice of research tools. To acquire an under-
standing of folk or indigenous knowledge systems in a
natural way (Brokensha et al 1 980) e thn os cienti st s
participate' and live in the culture they are observing,
often for extended periods (Spradley 1979). To test their
understanding, they model farmers' knowledge of the meaning
of important cultural symbols in the farming systems. This
indigenous or folk knowledge can be summarized and
represented in taxonomies, plans or scripts, goals, and
decision models. To describe and illustrate the usefulness
of these tools, we present models of farmers' classification
systems, decision processes, goals, and plans, and show how
we use them to understand and evaluate traditional farming
systems of family farmers in a north Florida county,
Gadsden. We conclude by describing how such knowledge can
be used to better design on-farm trials in a farming systems
THE STRUCTURE OF FARMINGC IN GADI3SDENI COUNTY
For the better part of its agricultural history, Gadsden
County's farming tradition has been based on shade, or cigar
wrapper, tobacco. At its height, shade tobacco was planted
on over 6,000 acres, produced over seven million pounds
annually, and "represented a 100 million dollar industry of
which 25 million dollars was. invested in land, equipment,
barns, packing houses, and operating capital" 'in a four
county area in Florida and Georgia (Womack 1976: 98). The
importance of shade tobacco in Gadsden is further
illustrated by the fact that despite its declining markets
dur ing the .late 1960's, shade still represented an
economic force in the county.
The last three complete census years (1969, 1974, 1978)
are illustrative of the comparative value of shade tobacco
to total farm production value. In 1969, farms numbered 443
units with 149, or 33.6 percent, involved in the pro-
duction of shade tobacco. The total value of agricultural
production for that year was $25,917,000. The value of
shade tobacco production for that year was $15,622,244. In
other words, one-third of the county's farms accounted for
two-thirds of the county's production value, on an average
of 33.1 acres of shade tobacco per farm. In 1974, despite
the drop in production, this crop accounted for almost half
(46.3 percent) of the value of agricultural production :in
the county. In 1978, the year after the final crop of shade
tobacco, total farm production declined further to
$19,069,000; and the value of tobacco production, now only
flue-cured tobacco, accounted for only two percent of the
total (USDA 1969, 1974, 1978).
"Shade," as a type of tobacco, was first developed
during the latter part of the 19th century. It was during
the 1890's that the area's tobacco industry- was being
revived through the production of "sun," or cigar filler,
tobacco (Womack 1976:99-101) It wa s soon~ discovered,
however, that the lighter colored, silkier leaves found
near the shaded base of the plant and on plants shaded
naturally by trees, brought the highest. prices at market
because these leaves made the best cigar wrappers (Womack
1976:101; Love 1940:3).' This discovery led to the
introduction of artificial shade to cover the plants, first
built of wooden slats to be later replaced by cloth
stretched over a wooden frame, to create a uniform crop. In
this way, "sun" tobacco (tobacco growJn under the sun) was
put under a shading structure and became known as "shade"
The development of the shade tobacco industry in Gaadsden
County was ideal for ecological, employment, and financial
reasons. Tobacco in general, and shade tobacco in
particular, is a land intensive crop. Because Gadsden's
farmland is ecologically distributed among relatively small
fields with rich soils separated by timberland, swamps,
hills, and other uncultivatable land (so that less than
one-half of- its land in farms is harvested), a labor-using,
land-saving crop was, and still is, ideal for-its geography.
Economically, shade tobacco required a large number of
man-hours in both fields and packing house because the
utmost care was needed to protect the individual leaves from
the slightest damage. For example, a perfect high-quality
leaf could bring in as much as $6.00 a pound. A small hole
on one side of the leaf could reduce its value to $1.50 a
pound; and small holes on both sides of the leaf could
reduce its value to as little as $0.30 a pound (W.T.
Lasley, personal communication). Shaae thus .represented a
major source of employment and supplied nearly 18,000
skilled and unskilled jobs for G~adsden County alone'
(Korsching and Sapp 1976:1).
Financially, shade tobacco was ideally suited for
Gadeden's small farmers since the money to buy production
inputs was supplied by the buyer who therefore established a
formal "forward contract" with the farmer. This
relationship was established because shade tobacco required
a large capital investment to produce. For example, input
costs increased from $1,250 an acre in 1955 to $3,000 and
acre in 1968 to over $7,000 an acre in 1977. At the same
time, however, the farmer's profit margin remained in the
range of $1,000 to $2,000 an acre, with increasing costs of
production (mostly labor) keeping the profit margin down.
In order to help reduce the tremendous financial burden on
the farmers and insure their own investment, tobacco
companies contracted ahead for a season's production and
supplied up to 75 percent of the input costs as an advance
on the season's profits.
Overall, the high costs of production effectively
disuaded non-contract growers from entering the market while
at the same time, a forward contract helped to insure a good
price for the farmer. Ultimately, because shade tobacco
could be grown on small fields, required large amounts of
hand labor, and had a relatively secure market until the
late 1960's, it wa s an ideal crop for the small-sized
operations in Gadsden County.
THE SHA2DE-CEN\TERiED FARYMING SYSTEM
Shade tobacco was also a part of a more general farming
strategy. Although shade tobacco received the most
attention, other commodities (e.g., cattle and corn) were
managed around the production of shade tobacco. Cattle were
maintained for their manure that wias added to the soil to
maintain soil structure and help the chemical fertilizers.
Corn was produced mainly for cattle feed. Interestingly
enough, while these two commodities had a direct economic
effect on the production of shade tobacco, farmers
frequently stated that the value of cattle and corn was
Associated onlyr with their benefit to shade; in and of
themselves, they were only break-even ventures. Therefore,
due to the hish investment costs of shade tobacco
production, a loss of shade at harvest could hardly be made
up with cattle and corn sales. As one farmer comment ~d:
"Y~ou couldn't pay tobacco debts with corn!"
In this farming system, the best land was put under
permanent shade with overhead irrigation, allowing for two-
to three-year fallow-rotation periods. For example, if a
farmer had a contract to grow 10 acres of shade tobacco, he
might have 20 or more acres under shade, with 10 of those
acres in tobacco followed by a fall crop such a~s pole beans,
and the other 10 acres in irrigated row crops such as corn.
After two years, he would rotate tobacco and corn. The
balance of land not under shade was used for pasture and
rainfed corn for the livestock operations. Through this
farming system, shade tobacco farmers became acquainted with
three different farming strategies: tobacco, row crops, and
STRUCTURAL CHANGE IN GAIDSDEN COUNTY
During the decade covering 1967 to 1977, farming in
Gadsden County began to experience the macro-influences of
national and international trends that have led, in general,
to concentration of production in American agriculture.
Factors such as mechanization, market competition at the
international level, and competition for land from non-farm
sources have all led to a general decline in farming in
Gadsden County and of the complete demise of the production
of shade tobacco in particular. The general trend towards
mechanization and modernization started with the gradual
replacement of hand labor for "stringing" tobacco on curing
sticks to the rolling of the cigars. The ultimate
innovation was, the development of synthetic or manu-
factured "homogenized" wrappers made from tobacco leaf
scraps held together by adhesives and a plastic tip so _that
a full leaf was .no longer necessary to bind the cigar
together. In addition, there were tremendous jumps in the
costs of production aqqrevated further by increasing labor
costs. The major jump occurred in the late 196=0's when shade
tobacco production had to come in line with minimum wage
regulations that it had, heretofore, been exempt. This
factor alone almost doubled the cost of labor, and applied
to all laborers: men, women, and children. This situation
was fur the r exac erbat ed by cheaper labor and production
competition from Central~ America where shade tobacco
industry was deve lo ped by the U.S. Gove rnment Some
Gadsden farmers also participated in the Central American
shade industry not only to increase their incomes, but also
to be able to compete with this new, lower priced market.
Lastly, despite occasional jumps in demand, for example
during the cigarette cancer "scare" of the mid 1900's, there
was a declining demand for cigars (Plath 1970:1-4).
Additional pressure on the G~adsden farming sector has
come from adjoining Leon County. More specifically, the
recent surge in development of the state capital area around
Tallahassee has been the source of competition for Gadsden
farmland from an expanding population seeking rural
The pillar of ethnoscientific tools is taxonomy, based
on the relationship "x, is a kind of y" (e.g., trees and
flowers are kinds of plants; oaks and elms are kinds of
trees; white and red are kinds of oaks: etc:.). More
formal definitions are found in Frake (1971), Kay (1971),
and Werner and Schoepfle (1979). Taxonomic analysis
searches for the internal structure of domains, which are
sets of cultural symbols that carry meaning for and to
members of the culture.
To understand how the Gadsden shade farmer considered
his post-shade cropping strategies, one must understand how
they thought about shade tobacco and what meaning shade
tobacco had in the culture of Cadsden County, which had,
after all, developed for 80 years around that crop.
Taxonomic analysis is one such method used to enter this
domain of knowledge. As Brush states, taxronomies can be of
"crucial" importance to understanding agricultural systems
because "..crucial decisions (regarding variety, where to
plant, and related inputs and technology> can best be
understood through the information matrix underlying the
folk taxonomy" (1980:37).
To find a substitute money crop for ex-shade producers,
a member of a farming systems team could consult the USDA
classification of different kinds of foreign and domestic
tobacco (Gardner 1951:18). But, because farmers' decisions
and survival plans depend on and are influenced by their own
knowledge or perception of tobacco, rather than the USDA's
knowledge of tobacco, a more useful approach is to
understand shade tobacco as the farmers do. Thus, an
ethnoscientist would elicit the classification structure of
tobacco internal to the Gadsden farmer.' Briefly, this
taxonomy (Figure 1) says that, first, Cadsden farmers
classify tobacco by use, into cigar tobacco (sun and shade
tobacco) and cigarette tobacco (f lue- cured and Maryland
tobacco) (Zabawa and Gladwin 1983; Zabawa 1984). At the
next level, shade tobacco, used for cigar wrappers, is
distinguished from sun tobacco, used for cigar filler.
Produced in Gadsden through the 1930s, sun tobacco
production declined as shade tobacco became more prominent.
Since the 1930s, the federal government has controlled
production by granting farmers the right to grow ilue-cured
tobacco in small areas or allotments, with a ceiling of 175
acres total in Gadsden County. Mzryland tobacco was briefly
introduced in the 1960s, but production declined shortly
thereafter when pressure from Maryland legislators forced
Gadsden farmers to include Maryland tobacco as part of their
flue-cured allotment. This action effectively killed any
attempt by G~adsden farmers to adopt Maryrland tobacco because
they had been growing it to increase their production over
and above their flue-cured allotment.
The lower taxonomic Icvelis further specify different
varieties of shade tobacco (Type 61l or Connecticut shade and
Type 62 or Florida shadc>, and different varieties or
Florida shade (R~g, Dixie shade, F1 shade, and the hybrids).
Partonomies or part-whole relationships then distinguish
meaningful parts of the individual plant for the farmer:
the roots, stalk, and leaves are important parts of the
tobacco plant. Because the shade leaves contain the
economic value of the plant, "sand" leaves (the bottom two
or three marketable leaves> are distinguished from the
"middles" (the next 4-19 leaves, among which the most
desired leaves are usually found), and the "tops" (the upper
two to four marketable leaves of the plant> (Kincaid 1960).
The taxonomic structure can be carried one stage further in
the marketability of specitice kinds of leaves. For example,
the highest quaity, and therefore the most profitable
"middles" were called "number one string" and sold -with no
further grading, whereas the rest of the leaves went through
a grading procedure developed by the tobacco companies (see
The taxonomy of shade: tobacco thus represents the
knowledge structure Gadsden farmers have developed while
growing shade. Farming systems team can consult
taxonomy' for possible substitute money crops. Indeed, the
second level taxa--fLue-cured and Maryland tobacco--would
have been logical alternatives it government controls had
not prevented increases in the production of these crops.
Gadsden-s farmers thus had to switch to money crops
out side the domain of tobacco. How did'they make that
decision? In most cses, they searched for and found
alternative crops such as tomatoes, nursery crops, or pole
beans and squash) that caused only a small disruption to the
original, formerly successful crop plan or farming system.
A knowledge of how they grow shade--their plan or
script--would be essential in identifying such similar
PLANS AN~D SCRIPTS
Instead of deciding how to do something every year,
farmers develop a plan or inherit a plan already developed
by their parents or grandparents. The plan, "how to do x,"
is a sequence of mental instructions or rules that tell the
actors who does what, when, and for how long (Werner and
Schoepfle 1979). To the insider or decision-maker, however,
they are not decision rules, because he or she is not awrare
of having had to make a decision. The decision is made so
frequently, so routinely, that the decision rules become
part of a pre-attentive plan or "script," like the script
in a play that tells the actor what to say and do (Schank
and A2belson 1977). By means of these scripts, the farmers
do not have to make a million decisions: they know how and
when to plant shade tobacco, probably because they were
taught by their parents.
Eventually, this knowledge will be passed on to a new
generation as a "traditional" way of doing things. When the
new generation of farmers is aLsked why they do the things
the way they do, they may reply, "it is the custom." Some of
them may even forget the original decision criteria; they
only know that, for some reason, the traditional way is "the
best" way to do x, given the original constraints or
constraints used or faced by their grandparents and parents.
Examples of such inherited scripts or "adaptive" strategies
abound in the literature of economic and ecological
anthropology (Bariett 1980; Bennet 1969; Brush 1976;
Cancian 1972; Chibnik 1981; Johnson 1971; Mayer 1979;
The Gadscden farmers; plan or script for shade tobacco
(Table 1) (K~incaid 19630) was quite similar to that for
staked tomatoes iTable 2). For example, tobacco seed beds
are planted and maintained in the same months when plastic
is put out for rows for tomatoes. Tobacco' seedlings and
tomato plants are transplanted in a similar, labor-intensive
way. In June and July, both tomatoes and tobacco are
harvested by hand; and, in August, fields are cleaned up
after harvests of both crops. Given the similarity of these
plans, it is not surprising that many ex-shade producers
decided to become tomato producers.
The importance of a plan or script as a tool in farming
systems research and extension is that it tells the
investigator something specific about the person or group of
people carrying out a particular action sequence. plans are
the highlights that show the outsider the insiders: methods
to achieve their goals and satisfy the roles that place them
within their culture.
HIERARCHICAL, DECISION MODELS
A knowledge of farmers' traditional cropping plans or
scripts, however essential to an FSE(/E team designing
on-farm trials, does not alwJays tell the team what happens
when the script or plan is interrupted or the desired goal
is changed. A knoniedse of farmers' decision criteria and
perceived alternatives and options is, therefore, necessary
to a team that want s to design adoptable ~technroloy or
evaluate technology already generated.
With this information, researchers can build models at
the decision-making process that incorporate -farmers'
decision criteria and constraints. The models of
decision-making are hierarchically (Gladwin 1976;, 1980)
ordered on the basis of the characteristic to be maximized,
incorporating alternative branches based on the constraints
and criteria of the farmers.
Hierarchical decision models !RDMs! are decision
" t r ees ," fl owc har t s lists, a set of rules, etc. For
example, given the shared knowledge about the different
cropping alternatives open to them, former Gadsden County
shade tobacco farmers have based their chance of crop
decisions on: knowledge and experience, especially of the
farming system shade tobacco cattle and corn discussed
previous'ly, and financial and market options.
A hierarchical decision model outlining this process is
seen in Figure 2. The decision criteria aspects,. or
constraints are denoted by the diamonds (C >) at the "nodes"
or the branching points of the tree. These criteria are the
goals "motivating" the decision, the aspects to be
"maximized" ~ or ordered on, or the constraints that must. be
passed or satisfied. In this case, the farmer must decide
between growing tomatoes, raising cattle, or cutting back on
farm participation. For example, the second criterion in
Figure is: "Do you want to grow a crop similar to shade
in managerial style and use of: resources: land, labor.
equipment, and capital?" If the farmer answers "yes," the
tree deterministically (with probability of 1) sends him--or
more accurately his responses--down the left hand branch of
the tree to consider crops very similar to shade. If the
farmer answers "no," the tree sends the farmer down the
middle branch, to consider crop not so similar to shade
ie.g., row crops>. If the farmer cannot pass the
constraints to row cropping, he is sent to the right hand
branch to consider dissimilar cropping .strategies such as
livestock-centered farming systems. If the farmer fails
this last list of constraints, he has no alternative but to
cut back on the farm operation by hiring a manager, getting
a partner, Leasing, or selling. The latter decision is
described at length elsewhere !Cladwin and Zabawa 19,83,
1484; Za$awa 1984).
Criterion 2 can also be thought of as an ordering aspect
in a stage twJo decision process
very similar to shade are considered before crops not so
similar which then preceded crops dissimilar-to-shade. Th se
same subsets of crops also share the same order on
profitability: in general, tomatoes and nurseries are more
profitable than soybeans wJhich are more profitable than beef
Each subset of crops has its owYn set of constraints,
which must be passed before a farmer proceeds to an outcome
!denoted byr Cj) npecifiying adoption of a particular crop.
For example, nurseries have a high capital constraint due to
the las time between initial investment and first returns
i criterion ) ; whi le t omat oe s have a high market risk
particularly, subdecisions with more in-depth stage one
criteria involved. For example, the fourth criteria
concerning tomatoes is also a more complete subdeci-sion that
includes the risk, market problems l and, labor, and
capital, particular to tomato production (see Zabacwa 1984).
If a farmer fails to pass constraints of a crop vr
similar-to-shade (and veyprofitable), he proceeds to a
not-so-similar and not-so-profitable crop and attempts to
pass those constraints.
The outcomes in Figure 2 show that, or the 52 farmers
who arew shade tobacco, there were 34 cases of farmers who
chose very similar-to-shade cropping strategies, 5 cases of
farmers who chose row crops, 6 cases of farmers who chose a
cattle-centered farming system, and 35 cases at farmers wJho
decided to cut back on their farming operations. (It
should be noted here that a farmer can enter the decision
model more than once. This accounts for the number of ca~es
exceeding the farmer sampic size.)
The meaning of these results is seen more clearly when
they are compared to the data found in Table 3. Table 3
re resents the farming strategies chosen by the ex-shade
farmers the year after they quit producing shade tobacco
(column 1) and the strategies chosen by these same farmers
in 1982 (column 2). From Table 3, it is seen that at the
end of shade tobacco, the farm sample divided itself evenly
between similar-to-shade alternatives--40.4 percent and the
cut back alternative--42.3 percent, while only 17.3 percent
of the sample chose dissimilar-to-shade alternatives. By
1982, this trend continues, as seen in the second column of
Table 3. The number of similar-to-shade cases remains high
at 32.7 percent, while the number ofcases of farmers
employing the dissimilar-to-shade strategies have been
significantly; reduced to 3.8 percent. Conversely, those
cases representing farmers who have cut back have increased
to d3.5 percent. Because dissimilar-to-shade cropping
strategies of row crops and livestock proved not profitable
enough to support a large number of full-time farmers, there
has been a transition away from the dissimilar strategies to
cut back strategies.
The decision model in Figure 2 reflects the above
transition.. The model is composed of the total number of
decisions employed by the sample from the time they dropped
shade tobacco until 1982. This model shows that 34 cases,
or 42.5 percent, adopted similar-to-shade strategies and
that 35 cases, or 43.8 percent, adopted cut back strategies,
the model also shows that there are only 11 cases ror 13.8
percent) of farmers who have attempted a dissimilar-to-shade
farming strategy. These farmers are part ofthe 17.3
percent of the sample found in the first column in Table 3.
More importantly, this model examined along with Table 3,
shows that these particular farmers ended up adopting the
cut back strategy, illustrated by the decrease in the number
of dissimilar strategies and an increase in the number at
cut back strategies in the second column in Trable 3. Asain,
these results show that Gadsden's shade tobacco farmers
either switched to very similar cropping strategies to shade
or they got out of' full-time farming and became part-time
farmers or gardeners.
In conclusion, knowledge of the decision criteria that
the farmers consider important (riskiness,
capital-intensity, equipment and land requirements> is vital
for a team trying to identify a suitable substitute money
crop, as is a knowledge of their plans or scripts to grow
the crops in question. Further, it is knowledge that cannot
be picked up tor all substitute crops on a "quick and dirty"
five-day reconnaissance survey !Franzel 1983: Gladwin
1983); it requires follow-up survey using careful
procedures to elicit information trom farmers in a
systematic w.ay (Gladwvin 1979a).
THE PROBLEM OF STRUCTURAL CHANGE
Just as micro-level decision processes are formed within
contexts which "frame" the decision (Tversky and Kahneman
1981), farmers: problem solving occurs within an economy
influenced by macro-level of structural forces. In the case
of the Gadsden County shade tobacco farmer, the macro-level
forces of mechanization and te chno logical change,.
international competition for markets and escalating costs
or capitalization of production, all contributed to the
demise of a tradition-bound arming system and lead to the
re-structuring of Gadsden County farming..
THE TRAN~SFORMA9TION OF FULL-TIME SHAD~E PRODUCERiS
To illustrate the transformation of the Gadsden shade
farmers, the different farm work strategies they employed
during their last year of shade production are compared in
Table 4 to those strategies employed by the same farmers in
1982. For example, during their last year of shade tobacco
production, 981 percent of the sample farmers were
considered full-time farmers (defined here as an average of
40 or more hours per week of tarm work) and 1.9 percent of
the farmers wecre part-time defined as farm work averaging
at least 8 hours per week but less than 40 hours per week).
By 1982, full-time farmers represented only 36.5 percent o
the sample, part-time tarmers represented 21.2 percent of
the sample, and non-farmers (that is, those averaging less
than 8 hours per week of farm work) represented 42.3 percent
of the sample (the majority of these farmers were retired).
These data clearly show that there has been a move away- from
full-time ~farming; and a chi-squared analysis of these
"before and after" strategies finds this transition to be
significant i0( =44.96, p=0.001).
For the former shade farmer, the impact of these
macro-forces has not stopped with the and of shade
production, however. These forces also aftoct the choices
that are considered as shade replacement crops, as well as
what crop will ultimately be adopted as the shade
replacement crop. For example, from the taxonomy in Figure
1, flue-cured and Marviand tobacco were tobacco alternatives
to shade; however, government controls severely limited the
widespread adoption of these crops. Similarly, from the
plans or scripts in Tables 1 and 2, tomatoes are shown to be
verycompatible with the shade arming system;
unf ortunat e ly, c omp et it ion from the large producers in
southern Florida, California, and Mexico limits entry of the
Gadsden farmer into this market as well.
Concerning the influence of macro-level forces, it has
been shown that since the demise of shade tobacco in Gadsden
County starting in the early 1970s, a significant number or
full-ime armers have changed their tarming strategy and
adopted part-time, non-farm, and retirement strategies.
That is, through the data presented, it can be shown WHAIT
these farmers did atter shade: some maintained a full-time
farm operation and others cut back. However, an additional
concern now needs to be addressed, and that is NHYI these
farmers chose the paths to their ultimate 1982 outcomes
given their specific decision environment or context. For
example, it can be correctly hypothesized that: 1
specific group of farmers chose the part-time strategy
because they were in the superior position of having low
debts and high assets and wanted to keep those assets at the
least risk; 2) those farmers with high debts and low
assets would cut back and sell some land to regain
financial stability; and 3) those with a relatively stable
debt and asset position, but advanced in age, would seek
retirement. These assertions, how ever correct, elicit
further questions of why would these farmers pick the end of
shade tobacco to decide that their farming (economic)
position would become more or less stable in the future?
The-answer, or at least part of the answer, to this
question lies in the farming alternatives open to the
producers of shade after the demise of this crop,
alternatives directly influenced by forces. such as increased
competition and costs. Specifically, ex-shade producers
faced with the loss of their money crop had to consider a
ma jor reorgani zat ion of the ir t arming strategies that
included going out of business entirely or cutting bac~.k
production substantially if they could not find a
comparable high-valued and environmentally compatible
substitute money crop.
As discussed before, after the demise of shade tobacco,
a former shade producer had three main alternatives. He
could: 1) adopt a similar-to-shade cropping strategy ie.g.,
nurseries, tomatoes, polebeans and squash, etc.); 2) adopt
a dissimilar-to-shade cropping strategy (e.g., row cropping
or cattle); or 3) he could cut back on his farm
participation and lease his land, sell out, and/or find
off-farm work or retire. These statecries are outlined in
Tab le s 5 and 6. Tab le 5 rep re sent s the cropping
Ilternatives adopted by the ex-shade producers the year
after their last crop of s;hade tobacco and Table tj
represents the cropping alternatives adopted by the ex-shade
producers in 1982.
Three observations can be made concerning the fi-fty-two
former shade farmers the year after they ceased shade
production. First, those farmers who continued with a
similar-to-shade farming strategy in=21) had the most owned
acreage, the lowest debt, and the lowest debt-to-asset
ratio. Conversely, those farmers who cut back the year
after their last crop of shade (n=22) had the least o~n~ed
acreage, the least assets, the most debt, the highest
debt-to-asset ratio, and were the oldest in age. Finally,
those farmers w~ho adopted a dissimilar-to-shade cropping
strategy, while the youngestt in age, struck a middle ground
between the similar-to-shade and cut. back strategies.
By 1982, the picture presented above had changed
significantly. Though there has been the expected shifting
of the sample due to the adoption, rejection, re-adoption of
various alt ernat ives three observ~ations can be made from
Table 65. First, those farmers who were in the
similar-to-shade cropping alternative category in 1982
(n=17) continued to have the most owned acreage and the
highest assets; by 1982, however, they also had the largest
debt as well as a significant increase in their
debt-to-asset ratio. Second, by 1982, those farmers wJho
were in the cut back category (n=33) hacd increased by 50
percent, mostly from farmers who had initially adopted a
dissimilar-to-shade cropping strategy, had the lowest debt
and debt-to-asset ratio. Finally, those farmers who were in
the dissimilar-to-shade category in 1982 (n=2) had decreased
their owJned acreage and assets, but had also increased both
their debt four times and debt-to-asset ratio by over two
Tables 5 and 6 present a clearer picture of what the
shade tobacco farmer experienced in post-shade agriculture
vis-a-vis the cropping alternatives open to him. First, the
farmers' in the similar-to-shade category were in the best
position to continue farming. By 1982, however, these
farmers show the effects o" adopting new, capital-intensive,
and often unproven income generating, high risk crops.
Second, because of its low income generating potential,
there is move among farmers who originaliv adopted
dissimilar-to-shade strategies to enter the category of
farmers who cut back.. Finally, those tairmers who have cut
back on their ta~rm operations (e.g., part-time, non-tarm,
and retired farmers), have reduced their debt wJhile they
also have retained and even increased their assets.
It also becomes clearer that the farmers who, in 1982,
have retained the full-time strategy (n=19) have done so at
the expense of their equity, and those with the greatest
debt and asset movement are the farmers who have adopted a
similar-to-shade cropping strategy; in=37), particularly, the
easily accessible tomato alternative tn=8).
Thi s paper has presented examp le s of the use o
ethnoscientific tools and hierarchical decision models that
can be useful in programs designed to generate a tro riate
t ec hn ologry for small-scale family farmers through a multi-
disciplinary team effort. In designing on-farm trials,
farming systems researchers can benefit from knowledge of
farmers' indigenous classification systems, plans or
scripts, and cropping decisions. The case of Gadsden County
in the 1970s, when full-time farmers had to switch from
shade tobacco to tomatoes or go out of business, and the
case of Gadsden County today, where some farmers are trying
to switch from risky tomatoes to other alternative crops and
Part-time farming, shows the necessity of an in-de th
knowledge ot how farmers make cropping decisions and plans
along with a knowledge of: the context within which these
decisions are made.
In addition, the taxonomy illustrates the structure of
the G~adsden farmer's knowledge about tobacco, and helps to
pinpoint the logical alternatives open to farmers, given
the failure of one of the crops in the taxonomic domain.
Given government restrictions on alternative varieties of
tobacco as a replacement to shade, a knowledge of farmers'
plans and scripts to grow shade helps the researcher
understand the adoption of alternative cropping strategies
outside the domain of tobacco (e.g., tomatoes, nurseries,
pole beans, and squash). Finally, a look at the macro-level
structural forces affecting farmers today, such as market
competition and government regulation, helps to show how
such exogenous variables can influence the organization and
reorganization of a farming system as well.
We conclude that farming systems research and the design
of on-farm trials can help U.S. farmers solve their
problems if and only it some of those trials are oriented
towards the future farming systems in the country. Because
U.S. agriculture is so dynamic, it is not enough for a
domestic farming systems program to design ~a~rm trials based
on the knowledge of farmers current cropping systems. The
multidisciplinary- team should also be knowledgeable of
farmers' problems with the present cropping system, and
farmers. expectations of future cropping systems. Some of
the on-farm trials should be designed to help tarmers learn
about future farming systems that may come under considera-
tion in the future. The farmine systems team should be able
to assess whether and how many farmers will switch to these
new systems, and why~.
OtherT trials should be explicitly: oriented toward .the
part-time farmer in U.S. agriculture, because so manyr
full-time farmers in the less than $500,000 sales category
are transforming into part-time farmers (Sulauf 1984). Due
to the structural change in U.S. agriculture, the case of
Gadsden County is not an isolated instance. Unfortunately,
the majority of U.S. farmers, like the Gadsden farmers,
face dual problems of unstable world demand for their
products and the structural change in U.S. agriculture.
The Cadsden farmer has faced these problems and has adopted
post-shade tobacco cropping alternatives and also switched
to an emphasis on off-farm work and part-time farming. In
order for on-farm trials to help solve the problems of the
present day farmer, the farming systems team must first
understand the indigenous knowledge systems, cropping
strategies, and decision making processes of the farmers
they are trying to help.
FIGURES, TABLES, and REFERENCES
(T) Tobacco (T)
shade Tobacco Flue-cured
~(cigar wrapper) (cigarette -
er) (T) (T) filler)
Type 61 Type 62
(Conn. Shade) (Fl. Shade)
Rg (old) Dixie Florida
(1935)~ Shade Shade
er and bind
(T)) (T (T)
F1 15 F1 17 Fl 20 C80a C76 C63
(1964) (1968) (1968)
Sand Middles Tops
#1 String other grades
Figure 1: Taxconomy of Gadsden County Shade Tobacco
Can you withstand
of the hog industry?
Raise Hiogs ye
Success rate 1 0.812
Develop U-Pick/ 4-- I no 0css
TmteGrow 4-`- yes/ no: 45 cases
15 caeros Can you make a
(3 errrs) / living growing
no: 31 cases
(INursery; U-Pick; Tomatoes; Pole Beans, Squash,
Flue-Cured Tobaco; liogs; Row Crops; Cattle)
Figure 2: The Decision to
Change Crops after Shlade Tobacco
.N=101 cases of 52 shade and 21
(Did you grow shade tobacco 1 no aI ExtDcso1cae(s)
\as your maj or money crop? - ~~LIxtDcso 1css(s
boyou want to gro~
with similar manage
style and use of rt
land, labor, equipr
w a croP\ -Is the possible
erial a row crop center
sources: the possible pro
ment, and stock centered o
Or are you alreai
row cropping ver
no: 15 cases
red operation >>
fit from a live-
peration and >> 07
dy' set up for
65 cases /
Doyou have the capital
encouragement and inter-
est to develop a nursery
mo: 14 canner
Doyou have the acreage (>500
and equipment needed to row
Develop Row Crop I no: 30 cases
Is the possible profit from \11
a livestock centered (cattle)
operation >> 0 on your present
set up? Or are you already
set up for a livestock opera-
yesno 61 ass
develop Nursery 1 6 ns
I plato Do you have the\
capital to rtait
for u-pic~k/orchard /
Ce pterad Fanr
no: 35 cases
Are you willing to buy or rent \10
more land to increase acreage
along with any needed equipment /
'hTO yOU willing
to accept the risks
f growing tomatoes
'Are you willing to invest in\1
necessary livestock inputs
(buildings, fences, etc.) and
possibly increase acreage via
.purchase or rental for feed
and pasture to increase pro-
duction and profit?
Develop Livestock Cut Back:
Centered Farm I Sell, Lease,
O cases 35 cases
Develop Row Crop
,: 27 casee
Table 1 t Gadsden County Farmers' Plan for Shade Tobacco.
January 1st Plant seed beds.
Januiary- Prepare soil, fumigate, and fertilize with manure and
February chemical fertilizer.
Note: labor for the history of shade tobacco was local
with the majority of the laborers being Black.
March 1. Harrow soil into rows four feet apart approximately
three weeks before transplanting.
2.-Install shade cloth shortly before planting.
Late March- 1. Transplant seedlings in the shade.
Early April 2. Water at transplanting at a rate of 10 barrels of
3. Reset hills with missing or weak plants within a
4. Dust plants with insecticides on a seven day
5. Plow the rows twice a week (discontinue near harvest
time to prevent damage to the leaves).
April String plants (starting when plants reach one foot),
spirally from the stalk near the ground, to the
overhead wire above the row. Continue to string,
spirally between the leaves, once or twice a week
depending on rate of growth.
May Water when needed using overhead irrigation system.
June "Top" plants to prevent budding if desired.
July' Harvest seven to eight weeks after transplanting. The
harvesting procedure consists of:
1. Pick the desired leaves off each plant, i.e.,
"priming" (there can be 2-5 leaves per priming and
6-10 primings per plant).
2. Place the leaves in the order picked and haul them
to the tobacco barn.
3. String the tobacco in the barn.
4. Cure the tobacco in the barn (3 to 5 weeks).
5. Deliver the tobacco to the packing house.
August Clean up and prepare for a fall crop (e.g. polebeans)
Source: Kincaid 1960.
Table 2 : Gadsden County Farmers' Plan for Staked Tomatoes.
1. Prepare the soil, lime.
2. Order plants.
Note: labor for the preparation, transplanting, staking,
and stringing of the tomatoes is supplied mainly by
local Black residents. Harvesting is performed mainly
by migrant workers of Spanish descent from South:-
Florida, Texas, and Mexico.
1. Put plastic out on the rows (the plastic retains mois-
ture, prevents leaching of the fertilizer, prevents
2. Fumigate, fertilize.
1. Plants arrive.
2. Transplant into the fields around March 15th (plants are
watered through trickle irrigation that is under the
plastic; soil treatments are applied under the plastic
as well; plant treatments are applied through overhead
irrigation if available, or by portable sprayers).
3. Spray plant treatments on a five to seven day schedule
to prevent insects and disease.
1. Stake plants approximately two weeks after planting.
2. Start horizontal stringing approximately two weeks after
staking and continue on a two week schedule for a total
of four horizontal rows of string per row of tomatoes.
1. Complete stringing.
2. Irrigate as needed.
1. Start hand-harvesting the "green" tomatoes using local
and/or migrant labor and deliver the tomatoes to the
packing house for shipment.
(The harvesting cycle is to pick through one field, move
to the next field, let the fields rest and the tomatoes
mature, start picking again).
2. Start picking "pink" tomatoes when they represent about
10% of the tomato population--approximately two to three
days after harvesting begins (the "pinks" are harvested
by independent migrants who pay the farmer a flat rate
per box of picked tomatoes and then sell the tomatoes at
3. Open fields for u-pick operation at the end of harvest
and before clean-up operations begin (u-pick is saved
for last to prevent damage to the plants and the spread
of disease from other fields).
1. Burn the plastic string off the old plants with a 2--row
2. Pull up the stakes and store them.
3. Mow the old plants down and harrow them into the ground.
4. Prepare for a fall crop (e.g. pole beans) if desired.
Table 3 : Cropping Alternatives to Shade Tobacco.
Total 52 100.0 100.0% 52 100.0 100.0%
Note: aExamples are pole beans, flue-cured tobacco, squash.
Table 4: -Farm Uork Strategies for the Shade Tobacco Farmer.
Last year in ST 1982
ST Number Percent Number Percent
FTP 51 98.1 19 36.5
PTF 1 1.9 11 21.2
NF 0 0 22a 42.3
Total 52 100 52 100
X2 = 44.96, p =0.001
Note: al3 farmers retired
Tables 5: Financial Aspects of Different Post-Shade Cropping
Strategies After Last Crop of Shade (Nt52).
Strategy N Percent Age 0AC Debt($) Assets($) D/A(%)
Similar 21 40.4 49.9 466.7 24,650 328,693 9.70
Dissimilar 9 17.3 44.1 371.7 38, 889 359,811 15.56
Cut Back 22 42.3 51.5 283.7 53,015 247,238 23.30
Tables' 6: Financial Aspects of Different Post-Shade Cropping
Strategies in 1982.
Strategy N Percent Age 0AC Debc($) A4ssets($) D/A(%)
Similar 17 32.7 52.1 475.9 187,036 726,696 33.15
Dissimilar 2 3.8 58.0 106.5 150,000 300 ,000 37.50
Cut Back 33 63.5 62.1 246.0 6,364 263,026 2.41
Year after ST
Nursery 2 3.8 3 5.8
U-Pick 0 0 1 1.9
Tomato 7 13.5 40.4% 8 15.4 32.7%
P.B. F.C. SQa 5 9.6 2 3.8
Hogs 7 13.5 3 5.8
22 42.3 42.3%
33 63.5 63.5%
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