RprtChinSemSystem13
2. System Approaches in agriculture, some basics 1.
2.1. Introduction
Animals and crops are kept in so many ways and with so many different purposes that it is hard to
make sense of what is what. Consumers can have different interests than producers, and national
interests can be at odds with local priorities. One way to handle this variation is to study only parts of
the system, a so-called reductionist approach. These notes, however, take a more holistic approach.
They look not only at parts, but also at their inter-relations and surroundings, by using old and new
concepts of system thinking outlined in this chapter. A major reason to embark on system thinking is
that one simply cannot escape it. Everything is a system, even if other words may be used for it, e.g.
networks organisations or processes. A system can be a man, a woman (and their combination!), a
monkey, a scientific paradigm, a society, a meat processing chain, a cell, the galaxy, capitalism or a
religion. These notes use the term system thinking rather than system theory because that allows use
of concepts that are not yet formalised in a “grand” system theory (if at all that were possible).
However this variation in approaches and terminology also causes confusion. (box 2.1) This chapter
aims to clarify different approaches, terminologies, and their applications. More reading is available
from Von Berthalanffy (1967); Prigogine & Stengers, (1984); Klir, (1991); Jackson (1999); Checkland,
(1999); Schiere et al. (2004).
Box 2.1. Different forms of system thinking.
System thinking occurs in many forms, all over the world and through out history. Much of the
thinking in Europe and the modern America’s can be traced back to the ancient Greeks. But system
thinking is not confined to “western” universities or businesses, nor did it start in the “West”. American
Indians had their ways of system thinking before Columbus arrived there. And Africans had system
thinking before they saw the first Arabs or Europeans come in to settle, to trade and to bring their
religions (also forms of system thinking). Especially Asian cultures have a rich history in system
thinking. It is amazing how much the Greeks have in common with Hindus and they must have
borrowed from each other. Many Hindu notions even reflect modern notions on quantum physics and
non-equilibrium thinking. And the ancient books and thinkers from China and Korea such as the I
Ching and Confucius have insights still relevant today. Last but not least, also farmers can accumulate
vast knowledge on how (their) systems respond to internal and external factors. They do so by their
own observation and management of the herd, the crops, the soil and the people in the surrounding.
These notes use three strongly related and complementary forms of system thinking (hard, soft and
complex). A clear distinction between these is impossible, and each one has its place in Farming
Systems Research (box 2.2). However, much literature uses different terms for similar phenomena.
Terminology in system thinking is not well standardised across disciplines and many similar
phenomena have different names. For example, “context” can also be called “niche”, “local condition”,
“policy environment” or “boundary condition”. And a “model” is basically the same as a “metaphor”, an
“analogy” or a “homomorphism”. For that reason, these notes sometimes use different terms separated
by an oblique (“/”) to indicate similarity / convergence of meaning.
2.2. Different forms of system thinking.
These notes discuss different forms of Farming Systems Research (box 2.2), based on the use of
different forms of system thinking, and here classified as (the M stands for methodology):
1
Farming System Research for Dairy Development in China; Seminar Notes by Hans Schiere www.wur.nl
Wageningen University Research Centre (The Netherlands), Li Ou; www.cau.edu.cn/cohd, Gao Tengyun,
Henan Agricultural University (China) www.henan.edu.cn, and Bram Wouters Wageningen University
Research Centre (The Netherlands)
-
hard system approaches and methodologies (HSM),
soft system approaches and methodologies (SSM)
complex system approaches and methodologies (CSM).
As said before, much mainstream work on agricultural systems tends to focus on “parts”. It focuses on
either the rumen of the cow, the economics, the nutrient cycle or the DNA of the maize plant. That
approach is associated with the rather reductionist nature of HSM, and that is the starting point for
these notes. HSM tends to be reductionist and quantitative by its focus on numbers by ignoring
qualities and emotions. It also tends to focus on bio-physical issues of “matter” rather than on sociocultural or psychological issues of “mind”. However, analysis and design of farming systems has an
increasing need to understand the parts in their context, and to also understand more about sociocultural aspects. Such holistic approach sees a rumen as part of the animal, the animals as part of a
herd, and crops as part of society. This implies a need for what we call soft system methodology (SSM)
to grasp issues of mind, learning and society. SSM is particularly used in work with social organisations,
it does use qualitative information, and it includes “mind” issues. CSM is based on recent insights from
mathematics and thermodynamic theory in open systems. It is strongly related with so-called chaos
theory, or also non-linear system dynamics. This does, however, not imply that the approach is
“owned” by mathematicians and physicists. Notions of chaos were discussed at earlier stage by people
from philosophy and other sciences. (Klir, 1991).
Box 2.2. Farming systems research (see also Chapter 3)
An important type of system research in agriculture is Farming System Research (FSR) which itself also
occurs in many different forms, mostly with an explicit concern for the small farmer. We prefer the
classification of Simmonds (1986) who distinguishes:
- FSR sensu strictu: an in-depth analysis of Farming Systems, essentially an academic activity;
- OFR/FSP (on-farm research with a farming systems perspective), a practical way to test research
ideas on-farm, before recommending extension. This approach is closest to what these notes call
FSR&E;
- NFSD (New Farm Systems Development) which seeks to develop complex, even radical changes to
existing farming systems rather than the stepwise change characteristic of OFR/FSP.
One of the strategies to improve the quality and relevance of agricultural research has been the
introduction of the so-called “farming systems” approach to research and extension. It is also called
FSR&E or FSR&D to emphasize the need for linkages with Extension (E) and Development (D), or
OFR/FSP in the Simmonds classification. Farming Systems Research and Extension (FSR&E) is now a
well - established methodology and the main topic of these notes. FSR&E basically combines methods
and concepts from farm management, economics, from HSM, SSM and increasingly of CSM.
After getting used to the jargon one will see that HSM is complementary to SSM and vice versa. One
will also see that the relation between these two is bridged by CSM. We aim to de-mystify the terms
and concepts, moving from “hard” matter issues, via the “soft” mind ones to underlying concepts of
thermodynamic theory (TDT) 2 and system dynamics that represent the heart of CSM. This shows how
laws of TDT help explain the emergence, sustainability and evolution of (livestock) farming systems.
Ultimately the distinction between hard and soft, matter and mind, or biophysical and socio-economic
aspects will be seen to fade. The big paradigm shifts are in the move from positivist to constructivist,
from static to dynamic approaches, from equilibrium to non-equilibrium, or from linear to non-linear
thinking. These terms are explained in the rest of the text and a brief summary in given in table 3.1.
notions of TDT cannot be claimed by physics or mathematics; nor ignored by “mind” disciplines. Complexity
They underlie all system behaviour and they are thus reflected in all disciplines. Columbus does not “own” the
America’s because he discovered it. And mathematicians were not even the first ones to discover chaos-notions,
In the same way system thinking is also fundamental –and emergent- in all systems, not in either western, Greek
or Chinese cultures (see Schiere et al., 2004).
2
2-2
2.3. Some notions that “valid” in most forms of system thinking.
Most forms of system thinking share basic concepts. They are almost universal, but applied with
different rigor and interpretation, depending on place, time, form of system thinking and person(s)
using them. They are, among others, the basic notion of “something that can be considered as a
system”, relations, resource flows, inputs, outputs, system structures, boundaries, hierarchies and
fractals (with thanks to Sidney Luckett).
Table 2.1. Some “system-jargon” explained in very simple terms.
Main (set of) terms
Explanation
- complexity and
- complexity cannot be understood completely because it contains
complication
surprise, and complications can be understood by sufficiently researching
the issue because relations and parts are –assumed to be- few and clear.
- constructivism and
- constructivism is a way of thinking that stresses the need to combine
relativism
different viewpoints into a “constructed” worldview. It does no aim to
achieve one unambiguous “model” of reality. Relativism goes a step further
in saying that there is no reality, thus being perhaps the opposite of a
positivist.
- induction, deduction
- an inductivist approach continues to analyze until a general rule appears
to emerge; while deduction implies that one assumes existence of a
general law or value in order to make “prediction” about what will be the
result of a given action
- non-linearity and non- - the notion that systems cannot forever continue in linear fashion (growth,
equilibrium
speed etc.) without sooner or later showing curvilinear behaviour. It also
refers to notions that a present-day situation cannot always stay the same,
i.e., mode changes occur time and time again.
- paradigm (shift)
- a way of looking at the world, a worldview (Weltanschaung). A paradigm
shift occurs when one changes viewpoint, e.g. from positivism to relativism.
- qualitative,
- qualitative approaches are based on use of numbers, qualitative ones use
quantitative
stories, pictures or narratives. SSM tends to focus on the former, HSM on
the latter.
- reductionism;
- terms that together indicate a way of thinking which assumes that it can
positivism, objectivism
ultimately completely understand reality in an unambiguous and objective
way after studying it part by part
2.3.1. Different notions on system definitions
The “definition” of a system differs between forms of system thinking, even though they all share some
common ground. Since these notes are written from a complex-system approach we even have
difficulty in using the notion of definitions. We prefer to characterise rather than to precisely define
since we think that systems are changing constantly. In addition, we are constructivists, implying that
universal definition is not useful or possible. Only paradigms that see systems as static can agree to a
definition, all others should characterise rather than define. And that is how the following text should
be read.
2.3.1.1 The system as a unit (in space)
One approach is to consider a system as a rather static unit in space. In that particular case the
definition of a system is as follows:
A system consists of subsystems that together form a coherent whole, having a common goal and
boundaries, converting inputs into outputs.
Such a system looks like a box, and it is particularly used in HSM. The thinking focuses on use of boxes,
clear goals, well defined and measurable inputs and outputs. (fig 3.1)
2.3.1.2. A system as a process
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A more dynamic approach considers a system as a method / process in time. It states that:
a system is an organised or established procedure, method or process.
This approach looks in the first place at the way of doing things, the method / process of farming, food
production, marketing etc. It also implies aspects of “attitude” and “paradigm”. One can, for example:
- farm in a conventional or an organic way,
- do research in an inductivist and/or deductive way, etc
- be process or goal oriented
The term “farming style” typically relates to more dynamic and soft system approaches. It distinguishes
for example between cash-farmers and grass-farmers, machine farmers or traditional ones (Van Der
Ploeg, 1994). And in other farming systems literature this notion of style is implicit in the mention of
for example ley systems, upland systems, shifting cultivation systems (Ruthenberg, 1980).
“useful” produce
input
"waste"
Figure 2.1: A system as a unit where output can be "produce" such as milk, meat, eggs, draught, but
also "waste" such as dung, urine, heat. The term “waste” is put between inverted commas to indicate
that a waste can also be a resource rather than waste per se, depending on the frame of reference
used.
a)
fertilizer
b)
fertilizer
Land
Land
feed
feed
Cow
Cow
milk
milk
dung
Factory
Factory
by-products
processed milk
product
Consumer
Complaints
and praise
Fig 2.2: Different ways of thinking about “system structures”, when systems are considered as chains:
The upper diagram “a” represents thinking in terms of linear processes. The lower diagram “b”
represents inclusion of feedbacks and options for “learning” in a more non-linear fashion.
2.3.1.3. A system as a mode
A "mode" combines the notions of “unit” and “process”. It combines the notion of system structures
and processes in space and time! The term “mode” is used, e.g. in meteorology, where it refers to the
way weather patterns are organized and moving. In farming systems it refers to the way in which soil
particles, animals, crops, farmers, banks etc. are arranged, together with the way in which they behave
and interact over time (see also box 2.3). The term “mode” emphasizes dynamic aspects of farming
systems as they move in Einstein-like space-time notions. Slight changes in a detail combined with
other factors can affect a mode, the so-called butterfly effect. “A straw can break a camel’s back”, e.g.:
- rain beyond a threshold value causes the soil to be saturated after which run-off starts rather
abruptly;
- food below a critical value causes an animal to lose weight, until a point is reached where the
animal’s physiology starts to manifest itself in a different way.
- a slight change in prices does not always affect the way in which a farm is arranged or in the way it
functions. Beyond a certain point, however, the farmer goes bankrupt, animals and/or crop-stores
start to get sold, etc.
- systems close to the city are the same as those a bit further away from the city, until a “ givenpoint” where the mode of farming changes rather drastically. One can also see this in landscapes
2-4
(transects), where mode changes occur as one travels through regions with different climates,
altitude, soil type etcetera (Box 2.3).
The transition stage between two relatively stable modes is called a “messy” period when
rearrangement is going on but where the future state (mode) is still uncertain. It is the fear for this
uncertainty that makes many people hesitant to step out of present modes, even if they are not
anymore comfortable.
2.3.1.4. Relations, resource flows, inputs and outputs.
Much mainstream research tends to look at parts, and they neglect of ignore relations, i.e. structure.
The relations tend to be resource flows with one or anther form of information like energy, emotion,
skills, nutrients, water, hate/love notions and the like. Each of these is input as well as output for
different subsystems. Approaches that look only at one system (part) do work with inputs and outputs.
Taken in the stricter sense of the hard approaches they tend to mainly represent litres of milk, kilos of
feed. Stretching the mind (to where it should be in the soft approaches) the inputs and outputs lose
their strict meaning, but they would refer to resource flows such as skills, satisfaction, fears, hopes etc.
Box 2.3 A system as a mode, and mode changes in agriculture and society (picture quoted by
Bieleman, 1993)
Farming systems change as one moves from the city to the country side, due to changing prices
(scarcities) of resources like land, labour and capital, in association with changing attitudes of the
people. The German economist Von Thünen calculated the effect of such changed price ratios some
150 years ago (see figure above). He also “predicted” the associated farming systems, and till today
one finds this kind of processes around the world, also very strongly in China. Access to oil has made
the forest less essential as source of fuel and timber for city life, so that particular “mode” of farming
has moved towards (or beyond) the edge. The model is a simplification but the essence is a lesson to
be taken. As one moves from city to countryside there is a repeating case of mode-change, first from
free enterprise (often vegetable / gardening and urban dairy), then to forest, then to intensive
rotations and so on. As an aside, mode changes a regional scale and village can also be seen in
transects (see fig. 3.1 and 3.2), but also in fractal fashion at farm level, at plot and lower levels (see
2.3.1.6. In time they occur between ice age and warmer periods, between summer and winter, between
night and day, between early morning and late morning. And of course, they do not exist in space and
in time, but in space time.
2.3.1.5. System structure.
The structure of a system refers to how the parts are arranged in space and time (fig 2.3 and 2.4).
Indeed, when looking at parts alone one may overlook differences due to arrangement. For example,
the total number of pigs in the state of Iowa is the same now as it was some 10-20 years ago but the
number of pig producers had gone down considerably. This different structure cannot be seen only
from looking at the total number of parts (in this case of the pig sector). An example of structure in
time is that the average monthly rainfall in many semiarid regions can be good for crop production
but the distribution (= structure) over months and years is so erratic that crop production is a risky
business.
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2.3.1.6. Boundaries,
The hard tradition has a clear definition of boundaries, with examples of skins, tissues, watersheds,
geographical borders, bio-physical or socio-cultural limits to production, deadlines, harvest seasons,
the time (between growing and ploughing), etc. Soft and complex system traditions do recognize the
principle of boundaries, but they stress their flexibility. For example, where to put a boundary between
people, family groups and thought systems?
Fig 2.3 Different structures in mixed farming systems with different forms and levels of resource
exchange based on three farming system in Qindao (China):
a) a large strongly diversified commercial enterprise consisting of several rather independent and
linear (specialised) subsystems. Only a little exchange of “resources” takes place, like the use of
empty pigsties for calf-rearing and occasional use of cow- or pig-dung to fertilise the fishpond;
b) a mixed farm as an integrated enterprise with intensive resource recycling (web-likeness) at farm
level. Labour, feed, nutrients, grain, dung, etc, are exchanged between the parts of the farm as
found in many traditional farming systems.
c) a mixed system on village level consisting of several specialised dairy farmers that exchange dung
(for bio-gas and fertilisation) with specialised organic vegetable growers to get crop-waste in
return
Fig. 3.4: Networks in organisations as “structure” of usually socio-economic systems.
2.3.1.7. Hierarchies and fractals.
These terms are again interpreted in various ways but the basic notion is similar in many system
approaches. :
- hierarchies are different levels in systems, i.e., several cells make an organ, several organs make a
body, several bodies make a group / herd; several groups / herds make a community in space, and
several seconds make a minute in time etcetera.
- fractals reflect a principle where similar behaviours repeat themselves over different levels of
system hierarchy. It is often represented in drawings and real life phenomena such as fig. 2.6. For
example, in time one rests a few times per hour, per day, per week, per year; and in space a cell is
organized like a body, a body like a group of people, a group of people like a country
2-6
2.3.1.8. Models.
Models exist in a wide variety, they can be mathematical system diagrams as in hard system
approaches, they can be paintings, statues, metaphors, narratives, rich pictures, flow diagrams and
causal diagrams or even stakeholder maps in the soft system approaches. Basically a metaphor is an
analogy, and a more difficult word is homomorphism. The use of models helps because they are
simplifications of reality.
2.4. Hard systems methodology.
Hard Systems Methodology (HSM) tends to focus on notion of that a system is either a well-defined
unit in space (Fig. 2.1) or a strictly defined process / method / procedure in time (e.g. ISO, HACCP). By
precise definition and measurement the HSM aims to obtain what they call hard facts about the
system under study, to predict or to better understand the system and its behaviour. This is a useful
approach and/or approximation for certain conditions, for example where the conditions do not
change too much. The underlying idea that precise measurements are possible is not shared by the
soft and complex system approach.
A typical example of a system as a unit is an animal such as a goat that consists of a liver, a heart, a
digestive system, a brain etc. which together form a coherent whole (the pig), and that converts inputs
such as feed into outputs (dung, kids, mutton). Examples of such “systems” at other levels are:
- a liver that consists of individual cells and blood vessels,
- a farm that consists of pigs, cereal crops, fences, the farmer etc.,
- a landscape that consists of trees, farms, hills and roadsides,
- a group of people, an association, a family etc.
A system “as a unit” in HSM terms is described by defining boundaries such as cell walls, skin or fences,
by its internal components (livers, cows, cells, farmers), and by the main relations between these
components which can be structured in several ways (Fig. 2.3). However, together the parts are more
than the whole. This idea is very old, and partly caused by the fact that a system consists of
components (parts) and relations, i.e., not only of parts.
2.4.1. The usefulness of HSM.
HSM is particularly useful for example in calculating:
- nutrient balances and cycles,
- animal rations, and fertilizer requirements,
- cost / benefit ratios
- shed construction
- airplane manufacturing (even though that also requires soft system management!!).
Figure 2.5. Hierarchy in farming systems (based on Conway, 1987). Livestock and crop systems
are drawn as separate; in reality they interact in many ways and should have, an infinite number
of connecting lines.
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Figure 2.6. Examples of fractals, where a basic pattern keeps repeating itself into very fine and
increasing interesting pictures Left a fern, and right a “mandala” that is a mystical Hindu diagrams that
occurs in an infinite number of forms (See also Mandelbrot, 2000).
2.4.2. HSM and models.
The focus of HSM on "hard" data fits a useful tradition that makes formal models as in Fig. 2.7 and 2.8.
These are generally associated with the use of mathematical models that tend to describe how one
variable "y" (e.g. milk-yield of an animal or farm income) is determined by one or more "explaining"
variables xI (e.g. feeding, breeding, incidence of disease etc.). A common form of such mathematical
models is the use of simulation. Basically, that represents a farmer’s thinking about “what will I do if
prices continue to slide, if the incidence of disease continues to increase, etcetera. In essence, farmers,
business people and scientists and policy makers can use the same approach, at farm, regional or
(inter)national level.
government
$
chicks
manure
manure
market
grain
market
eggs
chickens
Figure 2.7. A resource flow chart based on the system language in which diamonds, circles and oher
symbols each have specific meaning (see Odum, 1983).
2-8
birth rate
# births
death rate
population size
# deaths
Figure 2.8. A system diagram based on the more time oriented system language developed by
Forrester.
2.5. Soft system methodology.
The use of SSM is mainly concerned with choices by organisations and people. It was developed by
people such as Vickes, Ackoff, Churchman and Checkland. In essence they understood that
organisations do not operate like machines and algorithms (and even those can fail). They saw that
different stakeholders had different perceptions of reality and based on such “complexity” they started
what is called the “soft system” thinking. The diagram in figure 2.9 is just a reflection of the fact that a
particular signal (the B or 13) is interpreted differently depending on the context. The term “soft” is
unfortunate because it suggests that mental or emotional phenomena are less “serious” than for
example a concrete wall or a fence of a farm. However, a hatred or fear can divide people more than a
concrete wall where a door can be made.
Figure 2.9. Effect of context on perception determines whether the central character is seen as a B or a
13.
Soft system approaches address situation in particular:
- where different “vague” and qualitative but real, human interactions play a role;
- where the opinion of one person affects the opinion of someone else;
- where different perceptions occur and where goals/ opinions change continuously.
SSM and HSM use different paradigms, but they are complimentary because SSM addresses so-called
"unstructured" problems that HSM cannot handle. SSM is particularly useful in situations where
decisions on problem, modelling language etc. are still to be taken. SSM can help to better understand
the different issues and aspects of “real life” as seen by different stakeholders. The notion of
stakeholders is central is SSM. It stresses that farmers, consumers, industrialists all are “stakeholders”
with different perceptions and priorities (fig. 2.10). Once goals, boundaries and approaches of such
human systems are characterized it becomes in principle possible for HSM people to effectively work
in practical problems of agricultural development. Different stakeholders have different values and
goals tend to change as soon as they are defined. SSM is better equipped to work with such "learning
systems" than HSM3.
2.5.1. SSM and the basic system notions.
Soft system thinking also uses notions like systems, boundaries and resource flows. However, it uses
them much less strict, or more creatively than HSM. (Checkland, 1999; Ison, 1997; Röling, 1997). SSM
stresses that:
- the characterisation (definition) of a boundary and goal of a system is vague and subject to the
personal opinions of stakeholders and definitions that tend to change during the investigation. For
example after an on-farm trial or discussion with the farmer(s) it is likely that both researcher(s)
people and animals can “learn”, as well as groups of peoples, herds, farms, plants, societies, etc.. Learning here
implies the immunisation of an animal, the change of attitudes, the restructuring of a farm etc.
3
2-9
-
ánd farmer(s) come out with a different point of view. This can change the original objective of the
trial during the process (Fig. 2.9). Farmers also suggest different interventions when talking with a
veterinarian than with an economist, a problem that cannot be overcome by use of elaborate
questionnaires.
SSM works with inputs and outputs even if they are hard to quantify as for example skills,
emotions and hate / love relations. HSM focuses o use of quantitative data. The difference and
similarities between these two approaches are illustrated by comparing the rich picture (Fig. 2.10)
of the SSM tradition with the formal system models from HSM (Fig 2.7 and 2.8). HSM can run into
problems when stakeholders argue, for example about whether dung is a waste or a resource! That
is why "waste" is put between inverted comma's in Fig. 2.1. SSM stresses that different and
conflicting opinions of the stakeholders need to be made explicit and used together in a
“constructed” rather than a “single reality".
Part of the difference between HSM and SSM is explicit in their system definitions. SSM would
characterize a system as follows:
a system is a construct with arbitrary boundaries for discourse about complex phenomena to
emphasise wholeness, interrelationships and emergent properties.(Röling, 1994)
Another difference between HSM and SSM concerns the thinking about system “goals” as phrased by
J.P. Sartre and later by Checkland and Scholes (1989):
a system does not have a goal but it is given one according to the context.
Change and uncertainty exist in any system, but SSM copes with uncertainty and multiple realities by
including the choice of the observer. HSM tries to cope with uncertainty by standardising conditions
and by attempting to exclude the effect of the observer (see the difference between on farm and on
station trials (table 5.1). SSM was “made” to handle management problems in business. It is now used,
supported by HSM, in community action on environment, watershed programs, setting of research /
extension priorities, etcetera.
NGOs
future generations
soil
government
animals
farmer
LIVESTOCK
AND
ENVIRONMENT
marketing
traders
landless
banks
extensionist
research
land owners
non-agricultural
community
livestock owners
local population
environmentalists
ecologists
unnatural concentration of livestock
production efficiency
deforestation
conservation of livestock wealth
overgrazing
income
- long term
- short term
soil erosion
LIVESTOCK
AND
ENVIRONMENT
women
2-10
human health
nutrient cycles
pollution
sustainability
action & effects
interactions
manure management
government policies
livestock management
Figure 2.10. Different stakeholders (top picture) and different issues (bottom picture) in the field of
livestock and environment; a “mind map” by participants of the 1999 livestock environment course at
IAC-Wageningen.
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2.5.2. SSM and models
SSM considers any model just as a disputable representation of reality. It uses formal system diagrams
as in HSM, also informal models like rich pictures, narratives or stories. SSM does try to quantify where
possible, e.g., through various forms of ranking (table 2.1.). But it also explicitly uses qualitative
information as in (modified) SWOTS and rich pictures (Fig. 2.11).
Figure 2.11. A rich-picture showing the variety of aspects and relations in a farming system.
Subsystems here are A: the fruit and timber tree around the house; B: machinery; C: the farm family; D:
the house; E: the tourist resort with hotel; F: the crop-fields; G: the village with school, shops and
community centre; H: a dungheap; I: the distant city where the truck takes milk etcetera. Arrows
represent resource flows of the “hard” and “soft” kind, e.g., nitrogen, water, sunshine, dung, milk,
emotions; skills education).
Table 2.1. Ranking of research topics by gender-differentiated groups (Lightfoot et al., 1990, in Jain et
al., 1995)
Research topic
Female group
Male group
Female group
Mixed group
interviewed by
interviewed by
interviewed by
interviewed by males
females
males
males
A
III
II
IV
V
B
II
III
I
II
S
I
I
III
III
D
V
V
IV
I
E
IV
IV
II
IV
2.6. Complex-system theory methodologies (CSM)
Complex system methodologies refer to the application of a theory of complex systems, not to a
complex theory of systems. The meaning of term “complexity” differs fundamentally from the term
“complication”. A complication can be understood after more or less hard work. But complexity cannot
be reasoned out, because it contains surprises and interactions that cannot be understood as yet, or
never. Complex-system theory is sometimes called “chaos” theory, and it uses concepts from physics,
mathematics as well as sociology and psychology. CSM studies and aims to predict the complex
changes of why and what happens in terms of both “matter” and “mind”, even more than SSM. It aims
to better understand how and why for example, an excess of produce like milk and eggs, or of “waste”
like dung start to affect the context, like the public opinion about agriculture. CSM works on the
notions of Conway (1987) about stability, resilience equity. It assumes conditions of ceteris imparibus4,
or constant change whereas HSM focuses on linear effects, such as the effect of fertiliser input on the
output of milk and dung from the farm assuming ceteris paribus5. It can work with non-linear
4
5
everything changing, including goals, contexts, relations etc.
everything else remaining equal
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responses but not with surprise. The CSM approach implies the use of a non-equilibrium paradigm,
versus the use of an equilibrium paradigm as in HSM where things eventually settle down, take
permanent form, and get “frozen” in space-time.
2.6.1. Complexity and uncertainty
One essence of CSM is, strange enough, that stresses both:
- variation, surprise, uncertainty and impossibility of perfect measurement.
- similarity, analogy, common patterns, repetition of certain forms and behaviours.
These notes stress that it is necessary to stretch the variation in observations so as start seeing
similarity. They use, for example, calculations and cases from often extreme situations to recognize
similarity. This requires exercise, but after some time it becomes easy. One learns to see “patterns”
due to an interaction between parts that are not seen when focusing on parts in isolation (fig 2.12).
CSM “invites” unexpectedness, creativity, and jokes (“accidents”), and in summary, it stresses that the
infinite number of known and unknown interactions makes a system complex to understand. A change
in one place affects systems elsewhere, changes are inherently unpredictable and a positive effect for
one part of the system can become negative for another part or at another level of system hierarchy.
What is "good" today may not be "good" tomorrow. If definitions were possible in CSM one would
word a definition for a complex system as follows:
a complex system is a system with innumerable emergent properties, hard or even impossible to be
defined boundaries and characteristics that are open for an infinite number of different
interpretations.
sun
market
feed
care
affection
genes
milk
meat
water
household
"waste"
(dung)
crops
rivers
fossil
reserves
Figure 2.12. A diagram to show how systems run on different resources, how they produce "useful"
product and "waste", how "waste" from one system is a resource for another etc. Note that the terms
waste and "resource" are ambiguous, i.e. their interpretation depends largely on the observer. In this
way it becomes impossible to predict the change in one part due to a change somewhere else. Even
the so-called dynamic models do discuss hange, but they use unchanged system structures to do so.
2.6.2. Simplicity in complexity, emergence of patterns, self organisation.
An important notion in complexity is the existence of both variation and similarity (repetition of form).
In practice it helps to increase the number of observations, i.e., to look for more variation, in order to
see order to emerge, or to see that phenomena start to repeat themselves. Such “order” is only
possible by expanding the range of observations, not by “digging” into the first observation and/or by
looking at parts only. The issue of the “B” or “13” in figure 2.8 can only be understood if one looks at
the context, not by understanding the ink that it is made of. Whether animal feed, plant nutrition or
community health is a problem tends to depend on whether the observer’s context is animal nutrition,
soil science or social work.
2.6.3. Self organisation, system emergence and thermodynamics.
Much stress was put, thus far, on uncertainty and different perceptions. Still, two unproven but
generally valid rules appear to govern system behaviour:
1st: it is impossible to create/destroy energy, only the form of energy can change,
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2nd: a system left to its own will tend towards greater disorder, measured as increased entropy
These so-called thermodynamic laws are fundamental to all systems, independent of their size or level
in the hierarchy, of whether studied with HSM, SSM or CSM. Particularly the second law implies that an
input is always required (maintenance) to form and/or to keep a system in shape. Importantly, this
notion goes two ways (fig 2.13):
- a certain system / form (A) requires a resource flow R,
- a resource flow R’ will result in a system / form A’.
In other words, without input a system is left to its own and it will degrade. Resources that are
transformed in a system (feed, energy, management etc into milk, eggs, draught etc) get downgraded
in the process. Only a part may be upgraded at the expense of further degradation of the rest. To take
some examples from animal production systems:
- a reasonably good feed conversion ratio is 2-3 kg feed/kg of meat for pigs and broilers (ignoring
other resources for housing, labour, raising of young piglets etc.). If the feed contains some 1.8 kg
dry matter, and if 1 kg of meat contains some 0.2-0.3 kg of dry matter then the conversion is more
unfavourable. The dry matter in meat, however, represents a “higher” form of order than 1 kg of
feed dry matter. Still, some 1.5 kg of feed dry matter is not converted into meat but transformed
from a higher form of organisation (mainly C6H12O6) into a lower form of organisation (mainly CO2,
H2O and heat).
- oil used in a tractor seems a small price to pay for “order” achieved by ploughing the land.
However, the oil is burnt and disorder is formed at a molecular level. This leads to a “disorder”
many magnitudes higher than the “order” achieved at macro level (Odum, 1972).
Figure 2 13. The relation between output and input, as well as between input and output.
The laws of thermodynamics have been known for many millennia to people who take the effort to
look around what happens. Both HSM and SSM tend to argue their validity, perhaps they both refuse
to believe that mankind is more subject to laws of nature than he/she may like. The interpretation of
the laws may change, but the principles appear to be grounded enough to count with their validity.
Some 200 years after the laws were first formalized it looks as if much system thinking about
agriculture still has to learn some lessons from the laws formulated by studying steam engines. Two
things are unfortunate in this respect:
- the tendency to think that the laws are for and by physics so that they do not apply to issues of
mind or human organisation. We stress that they are fundamental to system behaviour and that
they were first formulated in physics, but that they are not owned by physics. Studying the older
system thinkers from east and west it is easy to find notions on finiteness of resources and
resulting dynamics in system behaviours (box 2.4).
- space in these notes is too restricted to go into detail on these issues. We feel, however, that the
gist of these laws should be mentioned to be remembered. They are likely to drive changes in
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agriculture and society more than we may like, and policy is to consider them more rather than
less.
Box. 2.4. Social and other sciences, Columbus and thermodynamics
On the one hand, many people from “soft” sciences have difficulty to accept that a concept from
physics like thermodynamic theory can apply to socio-cultural system behaviour. But one might
suggest that TDT is as little “owned” by physics as that the America’s were “owned” by Columbus.
Physicists may have formulated the laws mathematically, but that does make them “own” the basic
concepts. It could be that socio-cultural disciplines have their own ways of (eventually) describing and
applying those principles to their own reality. On the other hand, colleagues from hard science also
still seem to have difficulty accepting the general validity of these laws when they believe that
technology will solve everything, as if energy cost and problems of waste are not inherent in the
behaviour of nature. There is an unlikely chance that the laws will eventually be disproved, or that new
approaches open new possibilities. Lord Kelvin dismissed the evolution theory of Darwin some 150
years ago on the grounds that there would not be enough energy, according to his thermodynamic
calculations to run the universe for as long as the duration of the evolution. He was unaware of the
energy in radio-activity. Still, it is wise to consider development options against the laws of
thermodynamics.
2.7. Concluding comments.
System thinking occurs in many forms. It is also done since ages, in the West as well as in the East. An
understanding of these different forms help to better use existing methods in system analysis and
design, it helps to see why people from different disciplines disagree, and how they could be made to
better understand each other. The classification hard, soft and complex is not the only way to classify
the various forms of system thinking. These notes start with the hard system approaches because that
is where many agricultural workers come from. That does not imply a higher order for had system
thinking than for soft and complex system thinking. The reverse might be true where the world
happens to be non-linear and complex, but where it can be simplified, sometimes, into more linear
and hard approaches.
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