Soedito Adjisoedarmo Fakultas Peternakan Unsoed Purwokerto
INTRODUCTION OF SYSTEM THE SYSTEMS VIEW The system view is a template for describing, analysing, and designing all aspects of any system . We will describe this view in organisational terms here because this is the viewpoint of a business manager. Reporting structures , sequences of work steps , information and material flows between work steps , and the organisation of data are modelled using the systems view .
What is a System? A system is a set of interrelated components that must work together to achieve some common purpose. Even when each component is well-designed, efficient, and simple, the system will malfunction if the components do not work together . Further, a change in one component may affect other components..
An example of what happens when sys-tem components do not work together appears in Figure 1. This house has all the components ne-cessary for a func-tioning home, but the rooms, plumbing, electrical wiring, and other compo-nents just do not fit together. The functional relationships among these components are simply not right. For example, front steps exist, but not where needed.
The process to develop a good system is called systems analysis and design ( SA & D). SA & D process are based on a systems approach to problem solving that is driven by several fundamental principles: 1) You must know what a system is to do before you can specify how a system is to operate. 2) Choosing an appropriate scope for the situation you will analyse greatly influences what you can and cannot do to solve a problem.
SYSTEMS, MANAGEMENT AND AGRICULTURE Introduction of System (Analysis) An organizational framework for systems Agriculture and the System Concept Model and Planning Methods
2) Choosing an appropriate scope for the situation you will analyze greatly influences what you can and cannot do to solve a problem. 3) A problem (or system) is actually a set of problems ; thus, an appropriate strategy is to recursively break a problem down into smaller and smaller problems, which are more manageable than the whole problem.
4) The solution of a problem is not usually obvious to all interested parties, so alternative solutions representing different perspectives should be generated and compared before a final solution is selected. 5) The problem and your understanding of it continues to change while you are analyzing the problem, so you should take a staged approach to problem-solving in which you reassess the problem and your approach to solving it at each stage ; this allows an incremental commitment to a particular solution, with a go or no go decision after each stage.
Function Before Form in Systems System are describe in various, and necessarily separate ways. These different ways concentrate on separate aspects of systems (for example, what the system does versus how it operates) or represent systems in different levels of detail. Consider a good example of system - a house .
As any architect knows, function precedes form with the design of a new house. Before the house is designed, we must determine how many people will live in it, how each room will be used, the lifestyle of the family, and so on. These requirements comprise a functional, or logical, specification for the house .
It would be premature to choose the type of materials, color of plumbing fixtures, and other physical characteristics before we determine the purpose of these aspects.
We are often anxious to hurry into building (form) before we determine needs (functions) , but the penalty for violating the function before form principle is increased costs– the cost to fix a function specification error grows exponentially as you progress through the systems analysis and design process.
Thus, the requirements of the house (or systems) must be well defined and clearly understood . Architects use blueprints and other drawings to depict and communicate the design specifications for these requirements. A blueprint is an abstract representation of the house , which mask many detailed and physical feature of the house.
Scope of Systems Often the fatal flaw in conceiving and designing a system centers on choosing an inappropriate system scope , apparently the designer of the house outlined each component separately, keeping the boundaries narrow and manageable; he did not see all the necessary interrelationships among the components.
Turning to a business situation (animal breeding is a business) , when a sales person sells a cheaper version of a product to underbid a competitor, that sales person has defined the limits of the system to be this one sale.
However, the cost of handling customer complaints about inadequacy of the product, repeated trips to install upgrades , and other possible problems make this narrow definition of scope inadequate.
The system boundary indicate the system scope. Defining the boundary is crucial to designing any system or solving any problem .
Fore example, we could install more efficient computer equipment that can process recording much faster, but if the staffs (recorders) of the recording center are confused by the equipment or if the human factors of using the equipment are not also considered as part of the system, any benefit from the new equipment may be lost.
Therefore, recorders and their capabilities should be included within the boundaries of the system being considered. Too narrow a scope may cause you to miss a really good solution to a problem. To wide a scope may be too complex to handle. Choosing an appropriate scope is difficult but crucial in viewing an organization as a system.
AN ORGANIZATIONAL FRAMEWORK FOR SYSTEMS Several useful frameworks exist to view how a system fit into the whole organization, and one such framework is illustrated in Figure 3.1
People Organization Structure Technology Task/ Procedure Figure 31. Fundamental Component of an Organisation
AN ORGANIZATIONAL FRAMEWORK FOR SYSTEMS This figure indicates four general key components of the organization that must work in concert of the whole organization to be effective , people, technology, task/ procedure, and organization structure .
The important point is that each time was change characteristics of one or more of these four components, we must consider compensating changes in the other. Fore example, when technology - such computer hardware and soft ware- changes , people may have to be trained , method of works may have to be redesigned, and old reporting relationships may have to be modified.
These change must be considered together, or we may find that the compenseting changes are infeasible or enacting them will take too long.
The framework raises as interesting question concerning making changes to organizations . In which of the four components to start ?
There is no universal answer to this. Issues of organizational politics can play a role in answering this question. When technology changes, we must consider compensating changes in the other components, we can use the technology change to make possible other innovation in organization.
CHARACTERISTICS OF SYSTEMS There are seven general system elements. Boundary ; the delineation of which elements (such as components and storages) are within the system being studied and which are outside; it is assumed that elements within the boundary are more easily changed and controlled than those outside . 1
Environment ; everything outside the system; the environment provide assumption, constrain, and inputs to the system. Inputs ; the resources ( data, materials, supplies, energy ) from the environment that are consumed and manipulated within the system. 2 3
Outputs ; the resources or products ( information, reports, documents, screen displays, materials) ~ provided to the environment by the activities within the system. Components : the activities or processes within the system that transform inputs into intermediate forms or that generate system outputs, recursively, components may be considered as the system themselves, in which case they are called subsystems. 4 5
Interface : the place where two components or the system and its environment meet or interact; system need special sub-components at interface to filtered, translate, store, and correct whatever flow through the interface. 6
Storage : holding areas used for the temporary and permanent storage of information, energy, materials, and so on; storage provides a buffer between system components to allow them to work at different rates or at different times and to allow different components to share the same data. 7
HERD IMPROVEMENT* OBJECTIVES (REQUIREMENTS FOR IMPROVEMENT ) Regardless of whether goals in specific breeds or seed-stock strains are for all-round merit or for specialised trait combinations, the general requisites for genetic improvement continue to be the same. First we must assess what we have genetically in our present animals .
OBJECTIVES (REQUIREMENTS FOR IMPROVEMENT ) This requires accurate records of performance on a large number of animals of known ancestry . Second , within seed-stock herds we must discover how we can increase the number of offspring form those individuals which have the desirable genes at the expense of the individuals with the less desirable genes.
OBJECTIVES (REQUIREMENTS FOR IMPROVEMENT ) Third we must optimise combinations of heriditary material from seed-stock herds for commercial production .
the general requisites for genetic improvement continue to be the same in specific breeds seed-stock strains are for specialised trait combinations,
the general requisites for genetic improvement continue to be the same for specialised trait combinations, assess what we have genetically in our present animals increase the number of offspring optimise combinations of heriditary material
future goals potential requirement of the consuming public. consumer needs per capita incomes strong demand for animal protein. annually 50 to 60 kg of beef, 27 to 32 kg of pork, and the equivalent of 270 kg of milk per capita,
additional meat needs from beef and swine increased numbers of animals . Efficiency in production per head increasing the production per animal. Milk
OBJECTIVES (REQUIREMENTS FOR IMPROVEMENT ) In developing future goals , animal breeder must continue to seek out the potential requirement of the consuming public. Insensitivity to consumer needs will be tolerated less and less as competition from substitute for animal products becomes keener and keener. The expanding United State population, with prediction of 250 millions by 2000 , suggests an increase in the demand for food products .
increased numbers of animals . increasing the production per animal. Milk heavy concentrate feeding is continued future competition of animals with human for cereal grains and other concentrated foods may be anticipated .
future competition of animals with human for cereal grains and other concentrated foods may be anticipated . selecting animals under conditions where the nutritional regimes have included liberal feeding of concentrates surplus of cereal
Secondary increase would come from the increasing animal numbers. Present milk production levels per cow cannot be maintained unless heavy concentrate feeding is continued. Hence, future competition of animals with human for cereal grains and other concentrated foods may be anticipated .
future competition of animals with human for cereal grains and other concentrated foods may be anticipated . selecting animals under conditions where the nutritional regimes have included liberal feeding of concentrates surplus of cereal
do best on high roughage best genotype for utilising rations dairy cattle and beef cattle
livestock industry market quality products recompense the producer for the extra quality special effort in breeding and production costs
AGRICULTURE AND THE SYSTEM CONCEPT Agricultural Production and The System Concept Many books are essentially about man's search for efficiency in the control of agricultural production . Most of the world's food and fibre is produced on farms ( under farming systems )
FARMS (farming systems ) world's food and fibre
world's food and fibre overall supply of food to the human population level of the individual farm or individual region .
FARMS (farming systems) large ecological framework exploited the natural Environ- ment. specialised farming systems
FARMS (farming systems) bio-economic complex controlled by man to achieve his economic objectives
FARMS (farming systems) bio-economic complex controlled by man to achieve his economic objectives increasing world population
FARMS (farming systems) bio-economic complex controlled by man to achieve his economic objectives increasing world population more specialised and consequently more biologically unstable FARMS (farming systems)
FARMS (farming systems) increasing world population more specialised and consequently more biologically unstable FARMS (farming systems) industrial products. severe biological problems
severe biological problems Many chemical inputs are not easily destroyed and accumulate in food chains affecting species which were no their original targets.
more specialised and consequently more biologically unstable industrial products. in the techno-logically advanced countries in the developping countries ----------- Disparities in the availability of food and fibre
On the individual farm, production processes systems standpoint . complex biological nature and their influence is essentially dynamic Econo mic Seasonal pattern demand supply
Seasonal pattern demand supply liquid milk management decisions taken to breed cows natural pasture growth cycles Econo mic Prices + production Prices + production
For all stages-Selecting the system Of feeding, housing, genotype, health care system operates When and How ? Sell or keep ? Sell or Keep ? When and How to mate ? When and How to mate ? When to cull ? How to market output ?
FARMS (farming systems) Production process Production process Production process In the management of farming systems it can be expected that an understanding of the links between the various components the production process will be at least as important as a knowledge of the separate components themselves .
MODEL AND PLANNING METHODS The agriculture scientist has employed a variety of mathematical models to explain the operation and organisation of farming processes and to enable predictions to be made about their behaviour. Many such models have been developed and here it is only necessary to record some important illustrative examples.
FARMS (farming systems) Production process Production process Production process Mathematical model Explanation prediction
The model of Crowther and Yates (19..) was of very simple nature and yet had a useful impact on agricultural policy in UK during World War II. The model took form: Y=Y 0 +d(1-1O -kx )
It purpose was to predict the yield (Y) of a crop for different levels of fertiliser application (x), assuming Y 0 is the yield with no fertiliser and d is the respond limit. Y=Y 0 +d(1-1O -kx )
A second simple model, which has had a wide influence on agricultural practice, was developed by Kleiber (19..) to estimate the maintenance energy requirement (E) of an animal in relation to its live weight (W) and constant (S): E= SW 0.75
Mathematical model had important roles in the development of agricultural practice their simplicity can be somewhat misleading Y=Y 0 +d(1-1O -kx ) ...... .... E= SW 0.75 ..... ......
Mathematical model had important roles in the development of agricultural practice policy determination and in production theory
Mathematical model had important roles in the development of agricultural practice policy determination and in production theory progress in planning farming systems
Mathematical model biological and economic detail the programming technique precise approach to decision making defined problem
Mathematical model biological and economic detail the programming technique Management problem time-dependent uncertain relationships optimal solution
Mathema- tical model biological and economic detail the programming technique systems analysis the model can be as complex as realistic
Mathema- tical model biological and economic detail the programming technique systems analysis the model can be as complex as realistic purely biological bio-economic
Mathema- tical model the model can be as complex as realistic purely biological bio-economic descriptive, analytical or constructive System research
In this terms, the model may be either purely biological or bio-economic in character and the objective of systems research may be descriptive, analytical or constructive .
Systems analysis Systems synthesis structure and functioning of a system the design and control of the new system System Approach/Research
Systems analysis Systems synthesis depending on observation of the system involving the use of established relationships to construct a system and examine its behaviour System Approach/Research
Systems analysis Systems synthesis Both, however, are dependent upon the development of an adequate model. System Approach/Research
Systems analysis Systems synthesis upon the Develop ment of an adequate model. System Approach/Research dictated by the purpose of the investi gation and the kind of problems to be solved
The justification for model building must be that experimentation with the model is more feasible and efficient than experimentation with and observation of the real situation.
ANIMAL BREEDING High quality world's food other forms of natural variation observed over some considerable length of time in different localities if being subject to climatic
In many cases the real system may prove too complex to permit suitable analysis from direct observation.
REAL SYSTEM OBSERVATION OBSERVATION SUITABLE ANALYSIS TOO COMPLEX TOO COMPLEX
REAL SYSTEM OBSERVATION So many factors may act in union and interact So many factors may act in union and interact cause some disturbance of the natural order
Animal protein world's food monitored and interfered by man FARMS (farming systems) BREEDING ANIMAL no monitoring is taking place.
FARMS (farming systems) ANIMAL BREEDING Management Experimentation with a computer model model building Animal protein world's food
Experimentation with a computer model model building the availability of computer time and the skill of the model building take place in a homogeneous or, perfectly controlled environment. V p =V g +V e
OBSERVATION Experimentation modelling cannot exist without some information based on experimentation and observation of real life situations REAL SYSTEM
modelling cannot exist without some information based on experimentation and observation of real life situations improved efficiency imparted to subsequent real-life experimentation. Areas of interest can be pin-point, and relevant treatment ranges established.
modelling cannot exist without some information based on experimentation and observation of real life situations cannot be restricted to any single presently-defined discipline improved efficiency imparted to subsequent real-life experimentation.
modelling cannot exist without some information based on experimentation and observation of real life situations a corporate effort by a team of specialists in separate discipline cannot be restricted to any single presently-defined discipline
modelling cannot exist without some information based on experimentation and observation of real life situations a corporate effort by a team of specialists in separate discipline an engineer and a computer programmer all working under the direction of a group leader.
PROBLEMS IN SIMULATING FARM SYSTEMS It will clear by now that the essence of the systems concept is to describe a situation with many interacting elements where, to be understood, any individual element in the system must be viewed in the context of the whole.
DirJen BinProd Luar Negeri Pabrik Semen Beku BPT &HMT Baturraden Pemasok Universitas Peternak Dinas Peternakan permintaan Materi genetik kebijakan Pejantan muda Data produksi Bibit betina laporan order konsentrat Data prod. Hasil penelitian Semen beku Bibit btn Permintaan bibIt Contex diagram
Flowchart pencatatan dan pelaporan produksi susu
Sumbang saran pemikiran ekspor ternak hidup (Contex diagram) Pengusaha Pedagang antar pulau eksportir ternak hidup pasar Malaysia pasar Brunei pasar Timur Tengah dollar Dirjenak Puslit bangnak Perguruan Tinggi dBase ( Simnak dll ) petani transaksi Ranch (pembesaran, bakalan) Rranch (pembibitan) Disnak kebijakan pembibitan kebijakan perdagangan ternak kebijakan permintaan bantuan sapi dollar sapi dollar Pasar dalam negeri laporan data/informasi data/informasi data/informasi data/informasi data/informasi penawaran penawaran permintaan permintaan Sumber bibit pelayanan permintaan PIR/Non-PIR perbantuan permintaan budidaya budidaya hasil hasil sapi dollar kerjasama kerjasama permintaan penawaran sapi sapi sapi dollar dollar HAL-HAL YANG PERLU MENDAPAT PERHATIAN Populasi ternak saat ini. Kemampuan produksi untuk jangka pendek dan panjang dalam mencukupi permintaan dalam negeri. Masalah yang berkaitan dengan pelestarian plasmanutfah ternak asli Indonesia Issue pemotongan hewan besar betina yang bertanduk Peningkatan pendapatan peternak dan devisa yang akan diperoleh 1
systems concept describe a situation with many interacting elements important implications for model construction
FARMS (farming systems) Production process Production process Production process model Explanation prediction OUTPUT
A subsystem whose functioning greatly influences the output criteria from the model should usually relative finer detail than one whose functioning has little effect on model output.
In this situation, the provision of data for model building is likely to prove a major problem. Generally the model will represent a marked degree of simplification and the relationships required in its construction will not always correspond either with the true relationships or with those directly available from research.
Some modification of research result will often be required. Even worse, few or no data may be available for some components of the model. In this situation is occasionally possible to generate the infor mation using the rest of the model structure.
CONTROL BY MANAGEMENT Broadly, farming systems may be classified into two types related to the degree of control that can be exercised over the production environment.
First, there are those systems where little direct control is possible. Generally the would be considered to be the more extensive type of farm organisation, characterised by low capital investment per unit of land .
CONTROL BY MANAGEMENT In such systems, control is mainly through a restricted range of management strategies such as adjusting the stocking rates, selling or buying stock, or perhaps modifying the soil environment by fertiliser application or crop rotation. By these means, the performance of the crop or of livestock can be controlled to some extent.
The second type of farming system is represented by production methods that attempt to reduce environment uncertainty by providing relatively large capital inputs .
CONCLUSION The few studies included in these covers can in no sense indicate the full scope for applying systems analysis to solving problems in agricultural management. Rather, it is hoped that what presented will be suggestive of the wide and so far un-exploited scope.
CONCLUSION It is fairly safe prediction that during the 1970s a great deal of attention will be turned towards analysis of agricultural systems as systems. It is anticipated that the present collection may help to light the way to an early and profitable attack on a wide front of the pressing problems in modern agricultural management.
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