Farmers’ experiments and scientific methodology


PAPER IN GENERAL PHILOSOPHY OF SCIENCE

Farmers’ experiments and scientific methodology

Sven Ove Hansson1,2

Received: 3 November 2018 /Accepted: 7 April 2019 /Published online: 15 May 2019

Abstract
Farmers all over the world perform experiments, and have done so since long before
modern experimental science and its recognized forerunners. There is a rich anthropo-
logical literature on these experiments, but the philosophical issues that they give rise to
have not received much attention. Based on the anthropological literature, this study
investigates methodological and philosophical issues pertaining to farmers’ experi-
ments, including the choice of interventions (work methods etc.) to be tested, the
planning of experiments, and the use of control fields and other means to deal with
confounding factors. Farmers’ experiments have some advantages over the field trials
of agricultural scientists (more replications, studies performed under the relevant local
conditions), but also some comparative disadvantages (less stringent controls, less
precise evaluations). The two experimental traditions are complementary, and neither
of them can replace the other. Several aspects of farmers’ experiments are shown to
have a direct bearing on central topics in the philosophy of science.

Keywords Farmer’s experiment . Agriculture . Field trial . Experimental control .

Confounding factor. Hypothesis formation . Indigenous knowledge

1 Introduction

By an experiment, in the common sense of the term, we mean a procedure with two
major components. An intervention is performed, usually under specified prior condi-
tions. In connection with it, observations of changes (or lack of changes) are made.
This is done in order to discover or verify what changes (if any) follow regularly after
performance of this type of intervention under similar conditions. Since experiments
are performed in order to gain knowledge about regular patterns of events, such a

European Journal for Philosophy of Science (2019) 9: 32
https://doi.org/10.1007/s13194-019-0255-7

* Sven Ove Hansson
soh@kth.se

1 Division of Philosophy, Royal Institute of Technology, Stockholm, Sweden
2 Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Uppsala,

Sweden

# The Author(s) 2019

http://crossmark.crossref.org/dialog/?doi=10.1007/s13194-019-0255-7&domain=pdf
http://orcid.org/0000-0003-0071-3919
mailto:soh@kth.se


combination of intervention and observations has to be repeatable (replicable) in order
to fulfil the epistemic function of an experiment.1

We associate experiments with science, but the practice of making experiments is
much older than modern science or its recognized precursors. Unfortunately, the
experimental traditions that are prior or parallel to science have escaped philosophical
attention. A major reason for this may be that their purpose differs from that of the
experiments that have mostly been at focus in the philosophy of science. With respect
to purpose, there are two major types of experiments, epistemic and directly action-
guiding experiments.2 (Hansson 2015) Discussions in the philosophy of science have
predominantly focused on the former category of experiments, whereas experiments in
traditions outside of (recognized) science belong almost exclusively to the latter
category.

An epistemic experiment aims at providing information about the workings
of the world we live in. Therefore, the regularities looked for are such that can
reveal mechanisms and propensities of the study objects. In typical cases, the
intervention introduces or eliminates some factor that is believed to contribute
causally to some effect, and the outcome is taken to confirm or disconfirm a
cause-effect relationship between the (type of) intervention and the (type of)
effect under the given (type of) circumstances. In contrast, a directly action-
guiding experiment has a practical purpose. It is performed in order to find out
whether some intervention can be used to achieve a specified practical purpose.
In this case as well, a successful experiment is typically taken to establish a
cause-effect relationship. However, the demands on that relationship differ. In
an epistemic experiment it has to serve explanatory or other epistemic purposes.
That does not apply in a directly action-guiding experiment, but instead the
effect of the cause-effect relationship has to be some desirable practical
attainment.

We can define a directly action-guiding experiment as the performance of
some action X in order to determine whether, or to what extent, a desirable
result Y follows, with the intention that this information should be useful on
future occasions when the attainment of Y is desired. The reference to future
usefulness is important since it marks the difference between performing an

1 The currently dominating notion of an experiment, as explained in this paragraph, was largely shaped by
John Herschel (1831, p. 76). It received influential support from John Stuart Mill ([1843] 1973, p. 382). In
everyday language, the word “experiment” is still often used in a wider sense. (“He is experimenting with
drugs.”) Some academic writers, typically among those not attending much to methodological niceties, also
use “experiment” in a wide sense that does not imply planned interventions and observations, but to the
contrary puts focus on the lack of planning and monitoring in certain human activities. This applies for
instance to descriptions of potentially harmful practices and developments as “real-world experiments” (Krohn
and Weyer 1994), “social experimentation” (Martin and Schinzinger 2009), “collective experiments” (Latour
2001; Gross 2010) and “massive unplanned planetary experiments” (Gore 2009). This wide sense of
experimentation will not be further referred to here.
2 To be fully precise, we should distinguish between epistemic and directly action-guiding interpretations of
experiments. It is conceivable for one and the same experiment to be used for both purposes, i.e. interpreted in
both ways. However, such overlaps do not seem to be practically important. (Hansson 2015, p. 92) It should
also be noted that both types of experiments aim at obtaining information (knowledge). For simplicity, the
term “epistemic” is used here to denote knowledge that explains or clarifies how and why things happen, in
contradistinction to knowledge about what will happen (possibly with an unknown mechanism) if certain
actions are taken.

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experiment and (merely) trying to do something.3 This definition also provides
a basic justification for requiring that these experiments should be repeatable:
Their purpose is to tell us how certain effects can be obtained in the future, in
other words to deliver useful action recipes. This is only possible if the
experiment discloses what will happen in general, under circumstances that
can be specified.

The X and Y of the definition can be of many different types. Experimentation can
aim at highly general knowledge that can be applied in a wide range of circumstances,
or it can aim at local knowledge that is only intended for use in some particular place or
situation.4

The vast majority of experiments in basic (curiosity-driven) science are epistemic.
This is true of the famous experiments that are often referred to in the philosophy of
science, such as the experiments by Mikhail Lomonosov and Antoine Lavoisier
showing the conservation of mass in chemical reactions, Léon Foucault’s pendulum
experiment, which confirmed the earth’s rotation, Gregor Mendel’s experiments with
crossing of garden peas, and Edward Morley’s experimental demonstration that light
has the same speed irrespective of its direction in space. Many discussions on the
history and methodology of experimentation only refer to this type of experiments.
However, a large part of the experiments performed in modern science are directly
action-guiding. Major examples of this are clinical trials, agricultural field trials, tests of
technological constructions, and various forms of policy experimentation.

As already mentioned, directly action-guiding experiments differ from epistemic
experiments in having a long-standing history that reaches back far beyond modern
science and its forerunners in classical antiquity. There is ample evidence that crafts-
people have experimented long before there was a learned experimental tradition.
(Hansson 2015) The same applies to farmers.5 It is inconceivable that agriculture could
at all have arisen and evolved without extensive experimentation. Indeed, the evidence
suggests “that people everywhere began by experimenting with the cultivation of plants
they were already collecting in the wild” (Bray 2000). Experimental practices have
been documented in traditional agriculture all around the world. In Europe, agricultural
experimentation was almost exclusively driven by the farmers themselves until the mid
nineteenth century, when scientists gradually took over. (Pretty 1991) The experimental
traditions in indigenous agricultural communities were scarcely noticed by Western

3 The distinction between experimenting and merely trying is in need of a more thorough discussion than what
would fit into this article. A major component of the difference seems to concern the purpose of the action in
question. Experiments are performed primarily, or at least to a large extent, in order to obtain (practical or
theoretical) knowledge for future use.
4 The field trials of modern agricultural science belong to the former category, since they aim at knowledge
that is general enough to be used at least by all farmers in a particular region or climatic zone. In contrast,
farmers’ experiments tend to be entirely focused on finding out what works on a particular farm or in a small
local community.
5 Claims have been made in the literature that some indigenous farmers perform “curiosity-driven” experi-
ments. (Rhoades and Bebbington 1991, p. 251; Pretty 1995, p. 181; Dixon 2005, p. 314) In the examples
provided, the curiosity seems to have concerned whether some desired outcome could be obtained with certain
means. Therefore, these experiments can still be included within the category of directly action-guiding
experiments. It is certainly not implausible that farmers sometimes perform experiments that are curiosity-
driven in the same sense as modern scientific experiments, namely driven by curiosity about the workings and
mechanisms of nature. However, I have not found any evidence of such experiments.

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scholars until the 1970s, when anthropological studies of experimenting farmers began
to appear. (Johnson 1972; Richards 1979).

Due to extensive field work performed by anthropologists and also by agricultural
researchers, there is now a large literature on farmers’ experiments. Most of it is
focused on practical issues, such as the potential for fruitful co-operation between
experimenting farmers and agricultural scientists, but it also contains a wealth of
information on how farmers in different environments and under different social
conditions perform their experiments. None of the other traditions of directly action-
guiding experiments outside of modern science, such as those of various crafts and
trades, seem to have been documented to a comparable degree. Therefore, the literature
on farmers’ experiments is a unique and untapped reservoir of information for studies
of directly action-guiding experiments outside of modern science.

The extension of the scope of study objects for the philosophy of experimentation
that is proposed here and in Hansson (2015) can be seen as a further step in a process
that has already been going on for about four decades. As was noted for instance by
Galison (1987, p. 2), Steinle (2002, p.2), and Karaca (2013, p.2), the major empiricist
philosophers of science, including Hempel, Braithwaite, Nagel, Popper, and Kuhn,
treated experimentation as exclusively devoted to the testing of scientific hypotheses.
This means that the experiments they considered are a proper subset of the epistemic
experiments performed in science. Ian Hacking’s (1983) book Representing and
Intervening was the starting-point of a new movement, which is now known as the
“New Experimentalism” (Ackermann 1989). Major tenets of this movement were that
experiments are in need of a more careful study than what they had previously received
(Franklin 1986) and that experimentation “enjoys a certain amount of autonomy from
theory” and “should be studied for its own sake, and not merely as subordinate to
scientific theorizing” (Karaca 2013, pp. 126 and 95). Starting in the late 1990s, several
participants in this movement turned their attention to exploratory experiments, i.e.
experiments searching for empirical regularities, performed without theory-based ex-
pectations on which regularities might be found. (Burian 1997; Steinle 1997, 2002;
Franklin 2005a, b; O'Malley 2007; Karaca 2013, 2017; Colaço 2018) However as can
be seen from numerous definitions and descriptions of the research area, this literature
still has an exclusive focus on epistemic experiments. Directly action-guiding experi-
ments performed by scientists, such as clinical trials, are rarely mentioned, and I have
seen no signs of awareness of any of the experimental traditions that prosper in
communities outside of modern science. (Ackermann 1988, 1994; Franklin 1986, pp.
103–104; Franklin 2005a, b, p. 3; Galison 1987, pp. ix-x; Gooding et al. 1989; Hacking
1983; O'Malley 2007, p. 339) The present contribution aims at a further widening of
the scope of philosophical studies of experimentation to cover all kinds of experiments,
both epistemic and directly action-guiding, whether or not they are part of a scientific
tradition.

In what follows, farmers’ experiments will be treated on par with modern scientific
experiments in a discussion of some of the major methodological issues that are
relevant for the reliability of experiments as guidance to practical action. Section 2
briefly summarizes the experimental activities performed by farmers: their extent,
purpose and forms, as well as the characteristics of the farmers who perform them.
Section 3 describes and analyses the means that traditional farmers have to identify
potential practices that are promising enough to be worth experimenting with. Section 4

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is devoted to the planning and actual conduct of experiments. It relates farmers’
experimental activities to well-known requirements on scientific experiments such as
controls, randomization, and replication. Both similarities and differences between
farmers’ experiments and modern scientific experiments are identified. In Section 5,
the differences are further discussed, and farmers’ experiments are found to have both
advantages and disadvantages, compared to the field trials conducted by agricultural
scientists. Section 6 provides some examples of how investigations of traditional issues
from the philosophy of science can gain from including the experimental traditions of
farmers in philosophical studies of experimentation. Section 7 concludes.

2 The prevalence and importance of farmers’ experiments

As already mentioned, farmers’ experiments have been reported from all over the
world, not least from traditional cultures that have had very limited contact with
modern science. In many of these cultures, experimentation is recognized as a specific
type of activity. Several languages have special words for “experiment”, such as “hugo”
or “saini” in Mende (spoken in Sierra Leone), “shifleli” in Bambara (spoken in Mali),
and “igerageza” in Kinyarwanda (spoken in Rwanda and neighbouring countries).
(Richards 1985, p. 98; Stolzenbach 1999; den Biggelaar 1996).6

In farming, experimentation is a necessity, not a luxury. This is because nature
changes and evolves. Due to natural evolution, pests and weeds never cease to pose
new challenges. The properties of cultivated soil change significantly over the years,
often as a result of the farmer’s own actions. Therefore, in order to feed one’s family, it
is not sufficient to rely on the knowledge of one’s forefathers. New methods and new
seeds have to be tried out continuously, just to keep pace with changes in the local
conditions. (Box 1988, p. 65) In sharp contrast to the common misconception of
traditional farming as unchanging, farming has always and everywhere been in an
unending process of change, largely driven by experimentation.7

Experimentation by traditional farmers is often an integrated component of a
sophisticated risk management strategy. Contrary to modern commercial farming,
traditional subsistence farming aims at minimizing the risk of food shortage, rather
than maximizing the expected yield. (Johnson 1972, pp. 150–151; van Beek 1993, p.
55) By constantly experimenting with many crops and varieties, as well as locations

6 However, like modern scientists, farmers often refer to experimental and non-experimental observations as
the same type of activity. The day-to-day activities of farmers offer many occasions for comparative
observations even without deliberate experimentation. The neighbour has cultivated the same crop in a
different way in an adjacent field. Children have sown at a shorter distance because they have shorter legs
or did not understand the instructions. (Stolzenbach 1994, p. 159) The distinction between such “unintended
experiments” and experiments in the usual sense is not always easy to draw, and need not be essential. For
instance, a Honduran farmer who was forced by circumstances to plant his beans later than usual saw this as an
opportunity to find out if later planting would have any effect on slug infestation. (Bentley 1994, pp. 143–144)
Arguably, his prospects for obtaining valuable information from these observations were about the same as if
he had delayed planting with experimental intent, although they were worse than if he had delayed planting in
only a part of the field.
7 The need for continuous change to cope with the effects of the evolution of other organisms has been called
the Red Queen principle, so named “after the Red Queen’s race from Lewis Carroll’s Through the Looking
Glass, in which the Red Queen and Alice have to run as fast as they can just to stay in one place”. (Alyokhin
et al. 2015, p. 345; cf. McCook and Vandermeer 2015)

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and practices, farmers spread the risks and make it as unlikely as possible that
everything will go wrong at the same time. This strategy has been reported from many
parts of the world. (Altieri 2002, p. 3; Brookfield and Padoch 1994, pp. 41–42;
Mokuwa et al. 2014, p. 14) Agricultural experimentation appears to be particularly
intensive in harsh and capricious climates where the need for well-developed risk
management is larger than elsewhere. For instance, traditional Andean agriculture
employs a “massive parallelism” in response to fickle natural conditions. It is not
uncommon for a family to have around thirty fields in different locations, each bearing
several crops. Experimentation aiming to find crops suitable for different soils and
climate conditions is an integral part of this form of farming. (Earls 2011) Farmers in
the Himalayas have developed similar strategies of diversity and intensive experimen-
tation in response to similar natural circumstances. (Schroeder 1985).

One of the most consistent features of indigenous agricultural experiments all over
the world is that novelties are first tried out on a small scale. New crops, varieties, or
methods are first tested on a small plot. (de Schlippe 1956, pp. 220–222; Lightfoot
1987, p. 81; Sumberg and Okali 1997, p. 38; González 2001, pp. 220–221; Bentley
et al. 2005, p. 99; Stone 2007, p. 80; Leitgeb et al. 2008, p. 14; Stone 2016, p. 5) This
minimizes the consequences for food supply if the innovation fails. Furthermore, many
more experiments can be performed in this way than if larger areas were used. In many
cases, the experimental plots are close to the home, which facilitates surveillance of the
experiment. (Johnson 1972, p. 155; Richards 1985, pp. 98 and 145; Pretty 1991, p. 139;
Bentley 2006, pp. 452–453; Kummer 2011, p. 68) Importantly, the use of small plots
confirms that this is experimentation and not “merely trying”. An attempt to increase
this year’s harvest would have to be large enough to make a noticeable difference in the
total harvest if it succeeds. In contrast, an experiment is intended to provide knowledge
that can be used to increase harvests in coming years. Therefore, an experiment can be
so small that it does not contribute significantly to this year’s harvest.

There is ample evidence that experimentation is ubiquitous among farmers. It is
something that almost every farmer does, not something performed only by a few,
especially dedicated farmers. (Bentley 1994, p. 143; Sumberg and Okali 1997, pp. 45–
46; Nielsen 2001, p. 97; Bentley 2006; Leitgeb et al. 2014; Vogl et al. 2015, p. 140)
Experimentation seems to be performed spontaneously, rather than as the result of some
exceptional stroke of genius. In one case, extensive experimental traditions developed
among a previously pastoral people when circumstances forced them to take up
farming. (Boven and Morohashi 2002, pp. 93–103) That being said, there are also
many examples of individual farmers who have taken a leading role in local experi-
mental activities, for instance by keeping a large number of crop varieties, bringing
seeds and ideas from other communities, or inventing new work methods. (Harold
Brookfield and Padoch 1994, p. 40; Cleveland et al. 2000, p. 382; Dixon 2005, pp.
313–314; Beckford and Barker 2007, pp. 124–125; Leitgeb et al. 2011, p. 359) Since
women perform much of the agricultural work in many traditional societies, it should
be no surprise that women are often the principal experimenters, not least in experi-
ments concerning breeding and seed selection. (Abay et al. 2001; Nasr et al. 2001;
Ong’ayo et al. 2001; Saad 2001, pp. 9–10; Boven and Morohashi 2002, pp. 69–71).

The ubiquity of agricultural experiments does not mean that they are equally well
developed everywhere. As already mentioned, experimental activities tend to be
particularly vigorous in regions where agriculture is more difficult and precarious than

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elsewhere. The social conditions of experimentation are also important. Just like
scientists, experimenting farmers need freedom of thought and innovation. These
conditions can be threatened in a hierarchical system whose members are socially
expected to do as their elders, and discouraged from trying something new. At least one
example is documented of a traditional culture where experimentation has been
hampered by such limitations. (Nielsen 2001, pp. 101–102).

3 Hypothesis formation

Directly action-guiding experiments can only be successful if there are plausible action
recipes available for testing. There are many examples in the literature showing that
causal and mechanistic understanding can guide farmers in the construction of exper-
iments. Perhaps most obviously, knowledge that plants need water has informed the
development of various techniques for watering and irrigation, including more unusual
methods such as planting in deep holes to reach groundwater. (Ong’ayo et al. 2001, p.
116) Often, observations of the effects of non-experimental actions and events have led
to hypotheses about intervention–effect associations that have then been tested and
shown to be valid. As early as 2000 B.C.E., farmers observed that the mahogany
species Indian lilac (neem, Azadirachta indica) has repellant and antifeedant effects on
many insects. This led to a range of useful agricultural practices, such as planting
Indian lilac trees close to crops in need of protection, and spraying crops with
preparations made from the trees. (Snively and Corsiglia 2001) A couple of recent
such discoveries have been recorded. Ramani Abe-bieli, a Ghanaian farmer, discovered
that his cereals (millet and sorghum) were much less infested by striga (witchweed)
after he had interrupted the repeated cultivation of cereals on a field by one year of
onion. He hypothesized that a preparation from dried onion leaves could curb the
growth of striga. Reportedly, the new method turned out to be effective, and it was soon
adopted by other farmers in his community. (Tambo 2015, p. 120) In another case,
farmers in Kenya, who had recently started to grow rice, cleared water fern (Azolla spp)
from their rice fields. Having done so, they discovered that plants grew more vigor-
ously than elsewhere around the heaps of weed that they had collected. This inspired
them to make experiments, and as a result they started to use water fern to fertilize their
kale and tomatoes as well as their rice fields. (Kamau and Almekinders 2009) This was
a rediscovery; water fern, which has a high nitrogen fixation capacity, has been used in
Chinese rice cultivation at least since the sixth century C.E. (Shi and Hall 1988).

These examples probably represent the way in which various methods for soil
improvement have been discovered and rediscovered by observant and experimenting
farmers around the world. Farmers have developed a keen understanding of soil quality
problems and a wide range of efficient measures to deal with them. These methods are
similar across the world: adding organic matter such as manure, weed, rotting stumps,
or wood ash, swiddening, fallowing, crop rotation, intercropping including leguminous
crops, terrace-building to reduce erosion on slopes, slowing down running waters to
enhance sedimentation, etc. (Winklerprins 1999; Jodha 1980) Such improvements have
been obtained with the help of generalizations based on careful empirical observations,
such as “this crop grows better if we add wood ash to the soil”. A generalization like
this can be classified as causal knowledge, albeit of a different nature than modern

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biochemical understanding of the same process, which refers to the role of potassium in
plant metabolism. Empirically based causal generalizations can be efficiently action-
guiding even if they do not refer to the objects invisible to the naked eye that are
referred to in modern scientific accounts of the same phenomena.

However, there are also examples showing that lack of mechanistic knowledge can
hamper the development of useful intervention hypotheses for experimental testing.
Most obviously, this applies to lack of knowledge about pathogenic organisms that
cannot be seen with the unaided eye. Without microscopes and other laboratory
equipment it is difficult if not impossible to develop distinctions between different
plant diseases. Whereas traditional taxonomies for plants and vertebrates are usually
both detailed and much in line with modern scientific terminology, traditional vocab-
ularies for plant pathology usually contain few distinctions. Plant diseases are typically
not perceived as distinct entities, and the characteristic symptoms of specific diseases
are not recognized. (Bentley 1989; Bentley 1994; Trutmann et al. 1996; Bentley and
Thiele 1999; Stone 2016) This is no surprise; the scientific classification of plant
diseases was highly uncertain until their connections with microorganisms were de-
tected. (Stevens 1934).

Without knowledge about microorganisms that cause diseases, it is difficult to
develop useful hypotheses about measures to cope with these diseases. For instance,
many Nigerian farmers have discovered that a blight disease in cassava (Cassava
Bacterial Blight) is particularly prevalent after heavy rain. This has led to the erroneous
conclusion that the damage on the plants is caused by the rain, whereas it is in fact
caused by a species of bacteria that are more easily spread in rainy conditions.
(Richards 1979, p. 29) This mistaken belief hampers the conception of plausible
hypotheses on how to prevent the disease. (It can be prevented with crop rotation
followed by planting of new cassava from uninfected cuttings.) (Lozano 1986) Al-
though farmers have in some cases discovered the usefulness of removing disease-
affected plants, the idea of doing so is much less obvious if one does not know that the
disease is caused by a microorganism spreading from plant to plant. Therefore, such
(phytosanitary) measures are often not taken when they would have been efficient.
(Trutmann 1996, p. 69) Similar problems apply to animal health. Without knowledge of
the causal agents of animal disease, it is difficult for livestock owners to devise
reasonable hypotheses on how to treat diseased animals. (McCorkle 1989).

Knowledge about insects is usually limited in traditional cultures.8 Although most
insects are visible to the naked eye, it is difficult to divide them into species. Traditional
(“folk”) classifications tend to be vague and to operate with much fewer categories than
there are biological species. (Bentley 1989) Furthermore, the reproduction and devel-
opment of insects (sexual reproduction, usually followed by a series of three or four
stages of metamorphosis with very different body structures), is typically not part of
traditional knowledge. Farmers who have not received modern biological education are
usually not aware that a caterpillar eating their crops is the same species as an adult
insect, which they also observe on the farm. (Bentley 1989, pp. 27–28; Price and

8 There are exceptions to this. Many traditional communities have detailed knowledge about honeybees and
some edible insects. (Bentley 1994, p. 178) Nigerian farmers have traditional knowledge about the life cycle of
the variegated grasshopper (Zonocerus variegatus). (Richards 1979, p. 29) The weaver ant (Oecophylla
smaragdina) has been cultivated in China for pest control at least since the fourth century C.E. (Huang and
Yang 1987)

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Gurung 2006) Farmers in third world countries often believe that pestilent insects are
generated spontaneously from the plants they infest. (Bentley 1989, pp. 26–27; Bentley
et al. 1994, p. 178) (In European science, the issue whether or not spontaneous
generation can take place was considered open until the 1860s, when it was settled
by specific experimental evidence and better understanding of biological processes.)
(McMullin 1987) The common misconception that all insects are harmful has often led
to indiscriminate and harmful use of insecticides. (Bentley 1989, pp. 28–29).

Not having access to microbiological and entomological knowledge obviously
makes it more difficult to develop promising hypotheses for pest control experi-
ments. Consequently, plant diseases have sometimes been attributed to completely
unrelated factors, such as lightning, halos around the sun, planting in the wrong
phase of the moon, or – worse – the “evil eye” of menstruating women. (Bentley
and Thiele 1999, p. 78; Sillitoe 2000, p. 4) Such beliefs have also persisted in
Europe, where witches and people with an “evil eye” were accused of causing
crop failures. (Lykiardopoulos 1981; Behringer 1995) Magical ideas and practices
were recorded in American agriculture in the 1940s, and they still have a central
role in biodynamical agriculture, which is mostly practiced in Europe. (Passin and
Bennett 1943; Smith and Barquín 2007; Chalker-Scott 2013).

However, there is considerable experience showing that traditional farmers tend to
be interested in scientific information for instance about microbes and insect pests.
They also use such knowledge efficiently to develop new methods of crop protection.
(Sherwood 1997) For instance, knowing an insect’s metamorphosis makes it possible to
control it in another stage than the one that attacks the plants. In one case, Bolivian
farmers had problems with grubs attacking stored potatoes. After learning that these
grubs are the offspring of weevils, they developed methods to control this pest.
(Bentley 1994, p. 145) In many cases, when farmers have learned that insects such
as wasps and fire ants, which they previously believed to be harmful, are in fact
predators of pest insects, they have stopped killing them. (Bentley 2000, p. 284)
Instead, they have often invented and experimented with ingenious methods to attract
these insects to pest-infested fields. (Bentley et al. 1994; Bentley and Andres 1996;
Sherwood 1997, p. 182).

Most of the action recipes that farmers try out in experiments are not innovations in
the strict sense of new methods that have never been used before. Instead they often
consist in trying something on one’s own farm that other farmers have already used
elsewhere. In other words, the vast majority of experiments test adopted technologies
rather than newly invented ones.9 Therefore, learning from others is essential for the
construction or choice of action recipes to be tried out in experiments. Farmers observe
what others do, discuss with them, and exchange seeds. They learn from neighbours,
but also from people they meet when travelling. (Boster 1986, p. 433; Sumberg and
Okali 1997, p. 51; Maertens and Barrett 2013, p. 356) Obviously, this learning process
works best if there is a free flow of information. Ideally, a village can function as a unit
of collective learning through experimentation. However, farming communities differ
much in the extent to which information is freely shared. In some communities, the

9 Early European patent systems recognized the “innovativeness” of adopting a new technology from abroad.
For instance, the French Patent Act of 1791 said: “Whoever is the first to bring into France a foreign discovery
shall enjoy the same advantages as if he were the inventor.” (Suchman 1989, p. 1291n)

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information flow is rather severely restricted.10 (Conley and Udry 2001; Maertens and
Barrett 2013) In particular in hierarchical societies, there is a risk of prestige bias in
social learning. For instance, some Indian farmers may be more inclined to learn from a
high-caste than a low-caste farmer. (Stone et al. 2014).

4 The planning and conduct of experiments

The anthropological literature provides material for interesting methodological com-
parisons between the experiments performed by farmers and those performed in
modern science. Two important caveats should be kept in mind when such comparisons
are made. First, it would be grossly misleading to compare farmers’ practices with the
ideals of modern science. It is much more interesting to compare farmers’ practices
with the actual practices of modern scientists, which may not always coincide with the
ideals promoted in the philosophical and methodological literature. Secondly, it should
not be taken for granted that the same methodological requirements are relevant in the
experiments performed by farmers as in those performed by scientists. The two
experimental traditions differ in important respects, for instance in the extent to which
experiments are replicated and in the intended application area for the experimental
results (this farm vs. a large geographical region). In this section, six methodological
precepts from modern science will be used as descriptive tools to characterize the
methodological practices in farmers’ experiments, namely using controls, changing one
variable at a time, following the original plan, randomizing, blinding, and replicating.

4.1 Controls

In the directly action-guiding experiments of modern science, such as clinical trials, it is
considered obligatory to perform a control experiment (control arm of the experiment).
In order to find out whether performing X will lead to Y, we do not only perform X to
see whether (or to what extent) Y will then take place. We also investigate whether (or
to what extent) Y takes place under similar circumstances but without X. This enables
us to determine whether X really makes a difference. In some cases, we have back-
ground knowledge from previous experience, telling us that without X, Y will not
happen (or only happen to some limited extent). However, such so-called historical
controls are considered to be unreliable, since circumstances may have changed in
ways we are not aware of. Therefore, it is strongly recommended to include a control
arm in all experiments.

In agricultural experiments, it is usually easy to include a control arm, in the form of
a control field. This method has decisive advantages over historical controls, since the
two fields will be similar in terms of weather conditions, pest infestations, etc. In
addition, the memory problems that can arise with historical controls will be avoided in
this way. In fact, much of the methodology used in modern controlled experiments

10 In modern agriculture, the social learning process has sometimes been hampered by commercial secrecy
and even disinformation. For instance, Stone (2016, p. 8) reports that seed companies in several parts of the
world have sold the same seed under multiple names so that farmers are kept ignorant of which seeds are
identical or different.

32 Page 10 of 23 European Journal for Philosophy of Science (2019) 9: 32



developed out of agricultural experiments that followed this pattern. (Conniffe 1990;
Pretty 1991).

Judging by anthropological studies, most experiments performed by farmers involve
some form of comparison. According to a large survey of farmers’ experiments in
Africa, about 39% of the experiments involved “a direct, side-by-side comparison”
between adjacent fields or two parts of the same field. (Sumberg and Okali 1997, p. 98)
The prevalence of control fields has been observed by many researchers, also on
other continents. (Johnson 1972, p. 154; Gupta 1989, p. 88; Richards 1989, p. 19;
Bentley 1994, pp. 143–144; Richards 1994, p. 168; Lyon 1996, p. 43; Quiroz 1999,
p. 120; Bentley et al. 2010, p. 131) However, it is an equally common observation
that farmers frequently use historical controls. This can either take the form of
comparisons with a particular plot on a particular year, or, more typically, reliance
on “the accumulated understanding of past farming performances and major
influencing factors such as rainfall”. (Kummer 2011, p. 68) According to agricul-
tural researchers who cooperated with female Rwandan bean farmers, some of these
farmers did not use control plots since they “‘carry the check in their heads’, that is,
they know how the plot should yield under given conditions.” (Sperling et al. 1993,
p. 512. Cf. Leitgeb et al. 2010, p. 745) The limitations of this method are not
unknown to farmers. Sumberg and Okali quote one Zimbabwean farmer who used
the historical control method. He argued that the years were similar enough, in
particular concerning the amount and timing of rainfalls, for such comparisons to be
reliable enough. However, he added that the peculiar pattern of rainfall in the year
when the interview was made would make the comparison precarious for his latest
experiments. (Sumberg and Okali 1997, p. 99).

Another common method is to compare one’s own harvests to those of neighbouring
farmers growing the same type of crop. (Lyon 1996, p. 42; Leitgeb et al. 2014, p. 58)
However, in spite of the visibility of other farmers’ crops and work methods, such
comparisons tend to be less reliable due to lack of information on the neighbour’s
farming practices.

4.2 Changing one variable at a time

The old dictum that one should change only one variable at a time seems still to have a
considerable hold over scientists. Indeed, “testing of only one variable at the same
time” has quite recently been described as one of the criteria that a scientific field trial
has to satisfy. (Kummer 2011, p. 69. Cf. Röling and Brouwer 1999, p. 156) In projects
involving cooperation between farmers and scientists, scientists have sometimes been
“frustrated” with farmers whose experiments have not satisfied the one-variable re-
quirement. (Ramisch 2014, p. 127) Reportedly, this is “one of the points that has [led]
research station scientists to dismiss farmer innovation”. (Saad 2001, p. 14. Cf: Bentley
2006, pp. 455–456; Bentley et al. 2006, p. 100).

From a methodological point of view, these attitudes among scientists are somewhat
surprising. It is known since long that the advice to change only one variable at a time is
oversimplified and in many cases highly inadequate. Both in epistemic and directly
action-guiding experiments, if the variables are not independent, then the one-variable
precept is misguided. The American plant ecologist Frank Egler explained this as
follows: “[I]n the physical sciences one can generally isolate one variable at a time

Page 11 of 23 32European Journal for Philosophy of Science (2019) 9: 32



for study, whereas in biology and even more in vegetation science, this is rarely
possible. In the physical sciences differences among replicate measurements are gen-
erally attributable to deficiencies of technique; in biology differences are to a great
extent due to fluctuations in variables assumed to be constant.” (Egler 1960, p. 235).

If the variables are independent, then the two types of experiments differ in terms of
the one-variable precept. In an epistemic experiment, it will usually be an advantage to
determine the effects of each individual variable separately.11 This can provide us with
fine-grained information about the causal mechanisms. However, this argument does
not apply to a directly action-guiding experiment, since its goal is to obtain some
desired practical outcome rather than to elucidate mechanisms. If we have a credible
hypothesis according to which we can obtain the desired result by doing A + B + C,
then we should test this combination, rather than each of A, B, and C separately. This is
also how directly action-guiding experiments, such as clinical trials, are performed in
practice. For instance, suppose that there are mechanistic reasons to believe that a
combination of three drugs, which is already used with success against AIDS, might
also be efficient against some other autoimmune disease. Then it is the combination of
the three drugs that should be tested in clinical trials, rather than each of the three
component drugs by itself. Similarly, suppose that a farmer has obtained a few seeds
from a variant of beans that grows very well in another region, which has larger
rainfalls in the growing season. (S)he may then form the hypothesis that an improved
harvest can be obtained by sowing this variety and also increasing the amount of
watering. There are good reasons to test this action recipe, which involves a change in
two variables (variety and water), rather than just testing the new seeds without
increased watering. In other words, there may be good reasons for experimenting
farmers not to follow the dictum “change only one variable at a time”.

4.3 Following the original plan

As was noted by Arthur Stolzenbach, farmers performing experiments differ from
agricultural scientists in that they “do not pin down the design and execution of their
experiments but adjust them during the run of the cropping-period.” (Stolzenbach 1999,
p. 171) Whereas such changes would be considered faults in a typical (epistemic)
experiment in science, from the farmer’s viewpoint they may be part of what makes the
experiment a success. Basically, the farmer’s purpose with the experiment is to find a
way to obtain a certain desirable result. If observations that (s)he makes during the
experiment indicate that an additional intervention is needed, then making that inter-
vention can serve the purpose of the experiment. For instance, if it becomes obvious
during an experiment with a new crop variety that it needs more water than the standard
variety, then it is sensible to water it, in order to see whether the revised intervention
“new variety + more water” yields the desired effect. If it does, then the experiment can
be a success. Refraining from extra watering in deference to the original plan (just “new
variety”) would be tantamount to a guaranteed failure to obtain directly useful infor-
mation from the experiment.

11 If the number of independent variables is large, it may nevertheless be impracticable to change only one of
them at a time. More sophisticated statistical approaches will then have to be applied.

32 Page 12 of 23 European Journal for Philosophy of Science (2019) 9: 32



Again, it is important to compare farmers’ experiments with directly action-guiding
experiments performed by scientists, rather than with their epistemic experiments.
Clinical trials are commonly required to have a mechanism for early termination if it
becomes clear before the end of the trial that patients gain from being included in one of
the arms of the trial rather than the other. (O'Brien and Fleming 1979) It is highly
plausible that similar practices are prevalent also in other areas where scientists perform
directly action-guiding experiments. For instance, suppose that a group of technological
scientists are testing a new mechanical device. Halfway through the experiment they
discover that the device will break down unless it is lubricated in a way not foreseen in
their original plans. We would expect them to lubricate it, rather than sticking to the
original protocol. In doing so they would not be irrational or unscientific, and neither
are the farmers who adjust their original action recipes during an experiment in order to
increase the chances of a useful outcome.

4.4 Randomization

Randomization is one of the cornerstones of modern experimental methodology. It is an
efficient countermeasure, not only against known confounding factors, but also against
as yet unidentified confounders. However, its introduction in modern science was
surprisingly late. In the nineteenth century, there was sporadic use of some precursors
of the modern method of randomization, such as alternate allocation, in which the first,
third, fifth etc. patients in a clinical trial are assigned to one group and the second,
fourth, sixth etc. to the other. (Hansson 2015, pp. 106–107) The modern method, in
which each participant is randomized to one of the groups, was developed by Ronald
Fisher (1890–1962) in the 1920s when assigning cultivars randomly to fields in
agricultural trials. (Conniffe 1990) The first medical study employing the method
was published in 1948. (Doll 1998; Marshall et al. 1948) It is now, after some initial
resistance, accepted as part of the “gold standard” for a directly action-guiding exper-
iment in medicine: the randomized controlled trial (RCT). (Hansson 2014).

Judging by the published literature, randomization does not take place within the
traditional practices of agricultural experimentation. To the contrary, in co-operations
between agricultural scientists and farmers, the former have often had to give up their
plans for randomization in order to achieve practical goals such as “keeping all plots of
a given legume together in one set or placing all the unfertilized treatments on one side
of a footpath.” (Ramisch 2014, p. 128) This should be no big surprise, given how late
and reluctant the introduction of randomization has been among professional scientists.

4.5 Blinding

Blinding is a highly useful countermeasure against the effects that the experimenter’s
beliefs and expectations (and those of the human subjects, if there are any) can have on
the outcome of an experiment. The first scientific study with blinded evaluators seems
to have been the investigation of Franz Mesmer’s alleged animal magnetism that was
conducted in 1784 by a commission of the French Academy of Sciences. (Sutton 1981;
Lopez 1993) In the nineteenth century, blinding was employed by critics of deviant
belief systems, such as dowsing and homeopathy, as means to disclose scams and self-
deception. Towards the end of that century, some researchers began to use it as a means

Page 13 of 23 32European Journal for Philosophy of Science (2019) 9: 32



to improve the accuracy of observations in experiments where “real” effects were
expected. After World War II, blinding became more common due to increased
awareness of the effects of bias in research. It has been part of the standard require-
ments on clinical trials since the 1940s. (Kaptchuk 1998; Hansson 2015, pp. 102–104).

Extensive experience, in particular from modern medical and psychological re-
search, confirms that blinding is an efficient tool to prevent misinterpretation of
experiments due to bias and other confounding factors. But nevertheless, the introduc-
tion of blinding has been remarkably slow in many areas of science. Still today, many
experiments that could easily have been improved with blinding are performed un-
blinded. One example is the evaluation of histopathological material from toxicity
studies, which many pathologists prefer to conduct without blinding. (Holland 2001;
Holland and Holland 2011) Another example is field trials in agricultural science. A
trial involving two varieties of seed that are indistinguishable to the naked eye can
easily be organized so that neither those who sow and grow the seeds nor those who
evaluate the harvests know which seed is which. However, in practice this is not how
agricultural scientists normally perform field trials. A recent study indicates that
blinding may have a large impact on the outcomes of agricultural field trials. (Bulte
et al. 2014).

There does not seem to be any trace in the published literature of farmers using
blinding in their experiments. This is much less surprising than that agricultural
scientists, with their access to methodological literature on blinding and its advantages,
have not either adopted it.

4.6 Replication

As mentioned in Section 1, experiments have to be repeatable (replicable) in order to
fill their intended function, which in the case of directly action-guiding experiments is
to discover and confirm useful action recipes. Strictly speaking, an agricultural exper-
iment cannot be exactly replicated, since the conditions for farming change from
growing season to growing season. However, with a reasonably wide understanding
of the notion of a replication, an agricultural experiment is replicated every time
someone performs a new experiment with the same seed variety or the same work
methods under similar conditions. In that perspective, farmers’ experiments are repli-
cated to a massive extent that is unknown in academic science. This is because, as
already mentioned, farmers around the world tend to try new methods experimentally
on their own farms, even if others have tested them elsewhere under similar conditions.

Agricultural researchers have often noted, not without surprise, that farmers treat
new seeds or methods obtained from researchers in the same way as new seeds or
methods obtained from a neighbour or an acquaintance. Before adopting the new
practice on a full scale, farmers try it out on small experimental plots. In this way
“every change of the system introduced by an outside organization will be first
screened and tested by the farmer”. (Dries 1991, pp. 227–228) This is a remarkably
ubiquitous pattern that has been observed all around the world. (Ryan and Gross 1943,
p. 18; Brookfield and Padoch 1994, p. 41; Lyon 1996; Katanga et al. 2007, p. 120;
Waters-Bayer et al. 2009, p. 240) This massive experimentation might seem superflu-
ous, under the assumption that the field trials and other investigations performed by
agricultural scientists provide all the information that farmers need. However, this

32 Page 14 of 23 European Journal for Philosophy of Science (2019) 9: 32



assumption may not hold. The agricultural methods proposed by scientists may have to
be adjusted to the specific conditions on the individual farm. As noted by the Scottish
agricultural writer James Caird already in 1852, “the detail is everywhere varied by the
judicious agriculturalist to suit the necessities and advantages of the particular locality”.
(Caird 1852, p. 502).

The ubiquity of such local adaptations can be seen from the introduction of a “new”
method for storing seed potatoes, namely to keep them in natural diffused light rather
than in complete darkness. This form of storage has been used traditionally by farmers
in Peru. In the mid 1970s, researchers confirmed that it improved quality, after which it
was introduced in some 25 countries. An extensive study showed that the vast majority
of the farmers who adopted this technology modified it in various ways to suit their
own facilities and budgets. (Rhoades and Booth 1982).

5 Evaluation of the methodologies

Both farmers’ experiments and the field trials performed by agricultural scientists are
directly action-guiding experiments. They also both have basically the same goals,
namely to improve agricultural practices in respects that are important to farmers, such
as yield (per area and per work hour), resistance to pests and climatic stresses, product
quality, and sustainability. Therefore, comparisons between these two types of exper-
iments are of particular interest.

One important factor that needs to be taken into account in such comparisons is the
immense scale of farmers’ experiments. Well over 800 million people are working on
farms.12 Probably, most of them perform experiments. Most of these experiments are
not particularly innovative. They may for instance be tests of new seeds obtained on the
market or as gifts, small changes in established work methods, or minor adjustments of
crop rotation schemes. As noted by Jeffery Bentley, “these experiments are essential for
adapting techniques in constantly evolving farming systems. Yet each individual
experiment is not very novel or useful. It is their aggregate effect over the long run
that gives them value.” (Bentley 2006, pp. 451–452).

The massive extent of farmers’ experiments impart to them two considerable
advantages over the field trials conducted by agricultural scientists. First, since farmers
perform their experiments on their own farms, they obtain information that is fully
adjusted to the local conditions. Secondly, farmers’ experiments satisfy the demand for
replication to a much higher degree than any type of experiments performed by
professional scientists. But on the other hand, farmers’ experiments also have features
that put them at disadvantage compared to the field trials of agricultural scientists.
Scientists use control fields more systematically, and only seldom refer to historical
controls. They also apply modern statistical and observational methods that reduce the
risk of incorrect conclusions for instance due to the influence of confounding factors.

Can the advantageous features of farmers’ experiments compensate for their less
stringent methods for evaluation and for their less developed control of confounding

12 According to the ILO (2018, p. 66), the world’s total workforce is around 3.300 million, and about 26% of
them work in agriculture. This would amount to about 860 million people working on farms. However, this
may be an underestimate due to non-inclusion of family members working on farms.

Page 15 of 23 32European Journal for Philosophy of Science (2019) 9: 32



factors? And conversely, can the advantages of scientific field trials compensate for
their lack of local specificity and their much lower rates of replication? These are
empirical issues that cannot be solved with armchair methodological considerations.
However, something can be said about the character of the empirical issues that are
involved in this comparison.

Agricultural scientists aim for fairly general action recipes, for instance new crop
varieties or work methods that perform well at least in a whole region. The extent to
which this succeeds will depend, among other factors, on how strong the correlation is
between what works under these different local conditions. A strong correlation will put
agricultural science at advantage, whereas a weak correlation will put it at disadvan-
tage. Obviously, the strength of this correlation may differ significantly between
different crops or types of agricultural practices. It will also depend on the natural
and agricultural variabilities within the region that the recommendations of agricultural
scientists aim at serving.

6 Connections with the philosophy of science

It should be clear from the previous sections that farmers’ experiments give rise to
interesting epistemological and other philosophical issues. As I will now proceed to
show with three examples, there are also strong connections with several prominent
topics in the philosophy of science. This is, of course, an argument to pursue the
philosophy of experimentation in a unified manner, rather than separately for the
different experimental traditions.

Since Hanson’s (1958) seminal remarks on the inherent presuppositions of experi-
ments, the theory ladenness of experiments has been a contested issue in the philosophy
of science. (Karaca 2013; Franklin 2015) Heidelberger (2003) argued that exploratory
experiments can be theory-free, but this was contested by Radder (2003). Several
authors, including Franklin (2005a, b) and Karaca (2013), have proposed to resolve
this disagreement by distinguishing between different senses or degrees of theory
ladenness. This debate has been focused on epistemic experiments, but chances of
finding examples of experiments that are theory-free, or as close to that as possible,
would seem to be greater if the search is extended to directly action-guiding experi-
ments. (Hansson 2015, pp. 91–93) In particular, this applies to directly action-guiding
experiments performed outside of scientific traditions, such as farmers’ experiments.

As mentioned above, much of the attention that philosophers of science have paid to
experiments has concerned their role in the testing of hypotheses. The question how the
outcomes of experiments can rationally change the epistemic status of hypotheses
(verify, falsify, corroborate etc.) is still a standard topic in undergraduate courses in
the philosophy and methodology of science. Contributions by the New Experimental-
ists have made it clear that experiments can have other roles than determining the
rational epistemic status of hypotheses, but no one seems to have denied that experi-
ments can legitimately be used for that purpose, and that this is in practice one of the
major uses of experiments.13 A large category of hypothesis-testing experiments have

13 However, it may not be the most common form of experiment. In some research areas exploratory
experiments seem to be more common. (Franklin 2005a, b; Hansson 2006)

32 Page 16 of 23 European Journal for Philosophy of Science (2019) 9: 32



been left out of these discussions, namely directly action-guiding experiments. A
directly action-guiding experiment, such as a farmer’s experiment, always has a
(practical) hypothesis in the form of an intervention, such as a new seed or a method
to ward of harmful insects, which is put to test in the experiment. In studies of the logic
of theory testing, it would be of considerable interest to compare the testing of
traditional, theoretical, hypotheses in science to that of practical hypotheses such as
“this new seed yields a larger harvest than the old one”.

Communication and decision-making at the science-policy interface give rise to an
abundance of complex epistemic and value-related issues. One of these concerns the
evaluation of experimental evidence for practical purposes, i.e. the use of experiments
to determine whether a proposed action-type, such as a medical treatment or a method
in social work, produces the desired results. Here it is crucial to observe that the
information needed for practical decision making is what effects the measure has under
what circumstances. Information about the mechanism underlying a putative effect
(“how-information”) is useful for this purpose only to the extent that it contributes to
answering the question whether this effect is actually produced by the intervention.
(Hansson 2014, p. 46) Unfortunately, this has often been misunderstood, in particular
concerning clinical trials, which are typically performed by medical scientists who are
also deeply involved in mechanistic studies and discussions. Prominent philosophical
disparagement of clinical trials and associated evidence-based methodologies is based
on the misconception that practical decision-making primarily requires knowledge
about causal laws connecting the proposed measure with the desired effect. (Cartwright
2012, p. 315; Cartwright and Stegenga 2011, pp. 295–296 and 313) If this approach
were applied to farmers’ experiments, it would be incomprehensible how successful the
action guidance obtained from these experiments can be. The discussion on clinical
trials and evidence-based policies would gain much from a clear distinction between
epistemic and directly action-guiding experiments and from the inclusion of farmers’
experiments as examples of how the latter type of experiments can serve their purpose
without yielding information about mechanisms or laws of nature.

7 Conclusion

We have seen that farmers in all parts of the world have traditionally performed
experiments to a massive extent, and they continue to do so. Anthropological studies
show that many of these experimental traditions are fairly sophisticated; for instance
they involve control fields and long-term trials. These experimental practices give rise
to important philosophical issues, some of which have been discussed above. We
should see the philosophy of experimentation as a philosophical discipline of its
own. Since some but not all of the practices it studies are parts of science (in the
common sense of that word), the philosophy of experimentation overlaps with the
philosophy of science but is not one of its subdisciplines.

Farmers’ experiments are directly action-guiding, i.e. they aim at finding work
methods (action recipes) that have desirable practical effects. They should therefore
be compared to directly action-guiding experiments performed by scientists (clinical
trials, agricultural field trials, etc.), rather than to scientific experiments aimed at finding
out the causes and mechanisms of natural phenomena. In comparison to the field trials

Page 17 of 23 32European Journal for Philosophy of Science (2019) 9: 32



performed by agricultural scientists, farmers’ experiments have the advantages that
many more replications are performed and that results pertaining to many more specific
local conditions are obtained. On the other hand, farmers’ experiments often have less
stringent controls and less precise evaluation methods. In some cases, the formation of
plausible hypotheses for experimentation is hampered by lack of knowledge for
instance about the causes of plant diseases (microorganisms, life cycles of insects,
etc.). Thus, each of the two experimental traditions has advantages that the other lacks.
Neither of them can replace the other, but there is a potential for mutual and respectful
learning.

The anthropological literature on farmers’ experiments is extensive and rich in
empirical detail. As this article has hopefully shown, it provides useful material for
investigations in the philosophy of experimentation. However, there is also a need for
empirical studies of farmers’ experiments that put focus on methodological issues such
as the choice of topics and hypotheses for testing, the use control fields, how con-
founding factors are conceived and dealt with, and the process of evaluating and
drawing conclusions from experiments.

32 Page 18 of 23

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
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European Journal for Philosophy of Science (2019) 9: 32


	Farmers’ experiments and scientific methodology
	Abstract
	Introduction
	The prevalence and importance of farmers’ experiments
	Hypothesis formation
	The planning and conduct of experiments
	Controls
	Changing one variable at a time
	Following the original plan
	Randomization
	Blinding
	Replication

	Evaluation of the methodologies
	Connections with the philosophy of science
	Conclusion
	References