SYSTEMS APPROACH IN ANIMAL SCIENCES
Introduction
The world population has increased considerably in the last decades, as well as the demand for more
quality food. It resulted in a higher pressure on land, and the discussion on how to use land for human
food, animal feed and bio-energy production became important.
Around 8000 BC, agriculture was born; since then, agricultural systems continue to develop due to the
growing number of people to be fed, the development of agricultural tools and the complex social division
of labour.
When people started to create villages and town, a division of production and consumption occurred
food was produced on agricultural lands, and consumption occurred in towns so food has to be transported
there.
In 1750, the industrial revolution started in England (in the meantime, word population went from
10million to 800million between 8000 BC and 1750). People started using coal as energy source, which led
to the development of powerful agricultural machines it resulted in less people had to work on land and
productivity increased.
Around 1950, agricultural research started to invest in increasing productivity per hectare of land, with
plant breeding to have highly productive plants and animal nutritionist that looked for the optimal diets of
animals. The introduction of artificial fertilisers, high productive crops, pesticides and other innovations
increased land productivity enormously so called Green revolution. Labour became more expensive and
production systems changed again due to the introduction of technologies e.g. mechanisation and
intensification, that caused less need for workers.
Current agriculture systems support a population of 7.8 billion people, but worldwide there are very
different agricultural systems, e.g. those that depend on local resources, human and animal labour, and
those that depend on import of resources e.g. seeds, fossil fuels, fertilisers.
The shift from an agricultural society towards and industrial society, in which only a low % of people works
in agriculture, resulted in the need to produce food at low price. The focus on low price resulted in
intensive highly productive systems, which caused environmental, social and economic challenges (climate
change, growing population, urbanisation, public health, animal welfare, dependency on resources). The
increased concern of consumers resulted in the decision to protect environment, biodiversity and animal
welfare and being more sustainable.
Sustainable development development that meets the needs of the present without
compromising the ability of future generations to meet their needs (Bruntland report).
CHAPTER 1 – SYSTEMS APPROACH
Scientific approaches
Roger Bacon in 13th century used an experimental scientific approach, which leads to separating the
problem from its context it allows to control all potential variables related to the problem and to
manipulate them.
René Descartes, 17th century developed approach consisting of: 1) break down a complex system of
interest in separate components; 2) study these components separately and understand how they work;
3) combine the knowledge about all components to understand how the entire system works.
This approach involves two ways of thinking: 1) reductionism, refers to the knowledge on separate
components (reductionistic approach), and 2) determinism, refers to the idea that knowledge of the
complex object of study con be based on the knowledge of its components.
, The reductionistic approach generates the know-how of how the components of the system work.
Research done with this approach is mainly aimed at improving labour, land or capital use efficiency and
increasing production levels.
Systems approach
Contrary to the reductionistic approach, a systems approach says that when a complex system is broken
down into its components, it loses its essential characteristics; a systems approach is based on 3 steps:
1. Description of the general problem situation in terms of its economic, environmental and societal
context, and formulate a research hypothesis (holistic). Holism means that the relation between
elements of a system, and the placing of the whole system in its context determine the
characteristics and behaviour of the whole system.
2. Detailed analysis of partial research questions (reductionistic). Each partial research question is
answered to find an answer to the general research question.
3. Interpretation of results from partial research questions in accordance with the hypothesis
formulated in step 1 (holistic).
The systems approach generates the know-why, so it allows to understand the system as a whole and in its
context. The context is very important because it influences how the system can be, e.g. location of the
system, regulations and policies that regulate how systems work; for example, rearing Holstein Friesian
cows in Kenya does not give good results as in the Netherlands because they are placed in a different
context where e.g. there is no high quality forage, there is a different temperature that might not be
optimal.
NB: a system is an entity that maintains its existence through the mutual interaction of its parts.
Characteristics of a system:
- System components, e.g. if the system is a cow, its components can be the different body parts and
organs that interact with each other.
- Hierarchy of the system (sub-systems and supra-systems).
- Boundary of the system, since each system is actually a component of another bigger system.
- Inputs, processes, outputs and feedback.
- Homeostasis, which acts to maintain the system in equilibrium when external factors disturb it.
- Synergy among components; synergy means that the whole system is more than the sum of its
parts. The special properties of the system that result from synergy are called emergent properties.
Hierarchy of the system: a system might contain different sub-systems as components, and each subsystem
also contains its own components. The system is also part of a higher system, called supra-system. For
example, a dairy herd is our system, its sub-systems are the individual animals that have their own
components (organs etc), and the supra-system can be the dairy farm or an agricultural region.
System boundary: the boundary separates the system from its context. Agricultural systems are always
open systems, so they interact with their context to maintain their existence, i.e. there are inputs and
outputs across the system boundary. For example, our system is a laying hen and we want to study its
energy metabolism. The boundary can be the external layer of feathers; inputs can be feed and water, and
outputs are faeces, eggs and heat. However, the behaviour and functioning of the system “hen” is
influenced by its context, e.g. the type of housing.
Homeostasis and feedback: every system has inputs that are transformed into outputs, and this needs to be
maintained in balance. This balance is reached thanks to feedback mechanisms; the maintenance of
equilibrium is called homeostasis. Most systems maintain equilibrium thanks to both positive and negative
feedback mechanisms. Positive feedback acts to increase the size or number of one or more components
,of the system, while negative feedback acts to decrease it, e.g. levels of a certain hormone if the system is
an animal.
Synergy and emergent properties: the whole system is more than the sum of its parts. It means that
studying the parts of a system individually cannot lead to a full understanding of the system as a whole. For
example, our system is water; we can study the properties of hydrogen and oxygen separately but we will
never discover the property “wetness”; we can only discover it if we study water as a whole.
Agricultural production systems like natural ecosystems, they consist of biotic (plants, animals) and
abiotic components (minerals, water..). Agricultural production ecosystems also include a human
interference, while natural ecosystems usually do not. Agricultural ecosystems include a lower number of
plant and animal species compared to natural ecosystems (less biodiversity), to maximise the yield of the
products. Plants and animals used in agricultural ecosystems also have less genetic diversity, and they
reproduce often through artificial selection and not natural.
Animal production systems they are a special group of agricultural production systems because they
contain the subsystem “animals”.
CHAPTER 2 – USE OF MODELS IN THE SYSTEMS APPROACH
Definition a model is an imitation of the real world.
Models are used in the systems approach to represent and visualise the system or problem of interest.
- Physical models usually scale models of e.g. buildings, cities, molecules. They are not used in the
systems approach
- Abstract models they use symbols like word models, graphical models or mathematical models,
and give an abstract representation of a system. They are used in the systems approach.
Graphical models (also called relation diagrams, because they show
relationships between components)
- Timelines (linea del tempo) they are schedules that
represent the occurrence of important events regarding the
problem of interest over time. For example, Phong et al.
(2008) used a timeline to represent the factors that
stimulated development of integrated agriculture-
aquaculture systems in Vietnam.
- Rich pictures (come quella che abbiamo fatto per i
pulcini) they are used to express the concerns or ideas of
different stakeholders and show the relations between
different aspects of the problem (a stakeholder is a group of
people that affect or are affected by the problem, e.g.
farmers, consumers, governments).
- Organisational diagrams rectangles are mostly used to
show the hierarchy and responsibilities of its components
(figura a fianco mostra l’organizzazione dell’uni). Producers Consumers
- Flow diagrams they show the flow of e.g. money, energy, nitrogen,
through a system. E.g., the figure shows the flow of energy through the Inorganic
nutrient pool Decomposers
main components of an ecosystem.
In case we are dealing with a flow in an agricultural system, we often use
a fixed set of symbols that have a certain meaning (vedere nel reader fig 2.4 e 2.5!!). One
, commonly used symbol language is the Odum language. To draw an Odum diagram, these rules
apply:
o Inputs come from the left and outputs go to the right
o Leaching of material to the soil is indicated at the bottom
o Volatilisation of material to the air is indicated on top
o Energy density goes from low to high
along the left to right diagonal, so the
producers are represented on the
bottom-left corner and consumers in
the top-right corner.
Word models
They are used during the first step of the systems
approach in order to define the problem and find a
research question.
An example of word model is the SWOT you put an
analysis of internal strengths and weaknesses, and external opportunities and threats in a matrix. It is
used e.g. when and industry has to study the improvements for a product or to study problems in
agriculture. It is important that the SWOT includes the points of view of different stakeholders. The first
step in a SWOT analysis is to define the desired objective (e.g. improving a product), then the SWOT
describes the conditions and then possible strategies and action could be defined to solve the problem.
Example of a SWOT to determine if organic dairy production is better than conventional production (here
are showed strength etc. of an organic farm):
- Strengths farmer’s income because organic milk is sold for higher price, cow welfare, more
longevity, no use of pesticides/antibiotics, low occurrence of diseases, low reproduction problems..
- Weaknesses intensive labour for workers, land use (you need more land), more udder problems
(because no antibiotics used), risk of production losses etc.
- Opportunities good image for customers, regional production is more appreciated, strong EKO
label (organic product)
- Threats legislations are strict, uncertain availability of organic feed, straw, manure, knowledge
Mathematical models
They are generally used in step 2 and 3 of the systems approach. Examples are statistical models,
simulation models and optimisation models.
Mathematical models are used for:
- Characterization of:
o Systems e.g. number of animals, degree of intensification of farming
o Breeds e.g. weight, milk yield
o Products/labels e.g. fat content, production system
- Explaining/predicting the relationships between variables
- Comparing treatments in experimental designs (e.g. treatments for a disease) and comparing
different animal production systems
Statistical models: they are used to analyse observations and measurements of a system, its components,
relations, inputs and outputs.
