Upload
lwolberg
View
9.142
Download
25
Embed Size (px)
DESCRIPTION
Cristina Giosue proposes Donkey Milk for Italy--includes fascinating analyses of milk's energy content and systematic feedback analysis of animal husbandry.
Citation preview
UNIVERSITY OF PALERMO - SICILY (Italy)
Department S.En.Fi.Mi.Zo - Animal production Sector -
ANSC 400, CORNELL UNIVERSITY, 05/04/2006
Dr. Cristina Giosuè
A A System DynamicsSystem Dynamics application: “How to application: “How to increase the total donkey milk production in increase the total donkey milk production in
Italy, producing profitability to the farmers?” Italy, producing profitability to the farmers?”
PhD student in “PRODUZIONI FORAGGERE MEDITERRANEE” XVIII cyclePhD student in “PRODUZIONI FORAGGERE MEDITERRANEE” XVIII cycle
Visiting fellow at Cornell University in the Departmnent of Animal Science Visiting fellow at Cornell University in the Departmnent of Animal Science
OutlineOutline
-Introduction to System Dynamics System Dynamics modelingmodeling
-Examples of System Dynamics applicationSystem Dynamics application on a donkey milk production model
Introduction to System Dynamics modelingSystem Dynamics modeling
What is System Dynamics and why it is important?
- The complexity of the systems in which we live is growing (accelerating economic, technological, social, and environmental changes)
- Many of the problems we now face arise as unanticipated side effect of our own past actions
- All too often the policies we implement to solve important problem fail, make the problem worse, or create new problems
CSDNet
System Dynamics is
- perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems
- qualitative modeling method that enables us to build formal computer simulations of complex systems and use them to design more effective policies and organizations
- a rigorous way to help thinking, visualizing, sharing, and communication of the future evolution of complex organizations and issues over time for the purpose of solving problems and creating more robust designs, reducing the likelihood of counterintuitive or unintended consequences
What is System Dynamics and why it is important?
CSDNet
Figure 1-3 in Sterman Assumes a clear and linear response of results to our decision
Why unintended consequences?
• Event-oriented world view• Every event has a (single) cause• Leads to event-oriented problem solving
System Dynamics involve the use of:
• Diagrams, graphs, words, and basic algebra to activate and capture existing knowledge about a particular observed problem situation
• Frameworks to help both researchers and practitioners organize, filter and structure that knowledge
• Mathematical simulation models and learning environments that help researchers and decision makers to identify more sustainable solutions and further refine their conceptual models
CSDNet
“All models are wrong, some models are useful”
• All models are simplifications and must omit elements of reality
• Despite these omissions, “good models” can enhance our thinking and problem solving
All models have “clients”
• Clients are people you must influence for your work to have impact
• Help clients solve their problem……but also challenge their thinking
Examples of System Dynamics applicationSystem Dynamics application on a donkey milk production model
1. Problem Articulation(Boundary Selection)
3. Formulation4. Testing
5. PolicyFormulation& Evaluation
2. DynamicHypothesis
Iteration can occur from any step to any other
Modeling effort will go through each of these step many times
The SD Modeling ProcessThe SD Modeling Process
1. Problem Articulation(Boundary Selection)
3. Formulation4. Testing
5. PolicyFormulation& Evaluation
2. DynamicHypothesis
Iteration can occur from any
step to any other
Modeling effort will go through
each of these step many times
The SD Modeling ProcessThe SD Modeling Process
Real world
Information feedback
Mental models of real world
Strategy, structure, decision rules
Decisions
(Organizational Experiments)
The researchable PROBLEM is:The researchable PROBLEM is:
How to increase the total How to increase the total donkey milk production in Italy, donkey milk production in Italy,
producing profitability to the producing profitability to the farmers?”……..farmers?”……..
Problem articulation
……..and WHY?..and WHY?
Problem articulation
General problem (background)
Food Allergies
a) In the last 20 years the population of developed In the last 20 years the population of developed
countries suffering food allergies has increased countries suffering food allergies has increased
(from 5 to 10%), the children and the babies are (from 5 to 10%), the children and the babies are
the most affected the most affected (www.italiasalute.it)(www.italiasalute.it)
b) FA develop when intestinal immune system FA develop when intestinal immune system
doesn’t respond normally to the food proteins or doesn’t respond normally to the food proteins or
protein fragments which have escaped lumen protein fragments which have escaped lumen
hydrolysishydrolysis
Problem articulation
General problem (background)
Food Allergies
The main foods, that frequently cause allergy, are:
egg (albumin), peanut, walnut,hazelnut, fish,shellfish, cow milk, chocolate, fruit (strawberry, pineapple, orange, tropical fruit ecc), cheese and vegetable (tomate ecc) ecc.
The common symptoms of FA are:
nausea, vomiting, abdominal pain, distension, flatulence and diarrhea; sometime skin and respiratory tract may be involved. Occasionally, severe systemic (anaphylactic) reactions are provoked and these reactions may be fatal (Sampson et al., 1996; Kimber et al., 2002).
Problem articulation
General problem (background)
Cow Milk Protein Allergy (CMPA)
Cow milk protein allergy (CMPA) is the most relevant with higher social implications in developed countries.
Prevalence: 2-5% of the children and babies in the developed countries (Pizzin et al., 2003; Villoslada et al., 2005) and 1% in the adults (E. Smith, 1997).
Problem articulation
CMPA
Most children with CMPA synthesize Most children with CMPA synthesize specific specific
immunoglobulin E to proteinic antigensimmunoglobulin E to proteinic antigens (Iacono et (Iacono et
al., 1992),al., 1992), such as such as αα, , ββ and k caseins, and k caseins, ββ
lactoglobuline, lactoglobuline, αα lactoalbumine and lactoferine lactoalbumine and lactoferine (Teschemacher et al., 1997; (Teschemacher et al., 1997; F. Lara-Villoslada et al., F. Lara-Villoslada et al.,
2005; CMPA in infancy and childhood) 2005; CMPA in infancy and childhood)
General problem (background)
Problem articulation
CMPA
Actual alternative feeding
systems........ -Dietary products for infants derived from several
different protein sources (such as bovine casein, bovine whey, bovine or porcine collagen, soy, or mixtures of these) exposed to different procedures of hydrolysis and further processing (heat treatment or ultrafiltration)
-Dietary products based on amino acid mixtures
(Fiocchi et al., 2003).
General problem (background)
Problem articulation
CMPA
……..but..but
Some patients with CMAP can also react to Some patients with CMAP can also react to
these foods these foods ((multiple food allergies) multiple food allergies) (Iannolino (Iannolino
et al., 2005)et al., 2005)
General problem (background)
Problem articulation
Researchable problemThe donkey milk and CMPA
Since 1990, in Italy, some researches in regard to donkey Since 1990, in Italy, some researches in regard to donkey
milk use on children with strong food allergies have milk use on children with strong food allergies have
started.started. In some regions of Italy the use of donkey milk to feed the
babies is not new (in Sicily until the end of the Second War and in Germany) (Oftedal et al., 1988).
The economical, social changes and the industrial progress have caused an increase in the use of milk substitutes.
Iacono et al. (1992) showed good results by the use of donkey milk on 9 babies with multiple food allergies. The donkey milk was well tolerated and no negative reactions were recorded. These results were confirmed by Carroccio et al. (2000) on 18/21 patients.
Problem articulation
SpeciesWate
r%
Total solids
%
Fat%
Protein%
Casein%
Lactose%
Ash%
EnergyKJ/kg
Cow87.6
212.38 3.46 3.43 2.50 4.71
0.78
2983
Sheep80.4
819.52 7.50 6.17 4.50 4.89
0.92
5289
Goat86.7
713.23 4.62 3.41 3.10 4.47
0.73
3399
Cow buffalo
82.2 17.8 7.50 4.80 3.84 4.700.80
4846
Donkey*
91.16
8.84 0.38 1.72 0.38** 6.880.39
1939***
Woman87.5
712.43 3.38 1.64 0.40 6.69
0.22
2855
Milk composition (%) of different species and Energy (KJ/kg)
(Polidori 1994)
*Salimei et al., 1999; **Salimei et al, 2001; ***Polidori 1994*Salimei et al., 1999; **Salimei et al, 2001; ***Polidori 1994
Sheep Goat CowWoma
n
donke
yHorse
Protein (%) 95.3 91.3 95.0 88.6 86.0 90
Casein (%) 78.5 75.6 78.0 30.4 38.0 45
Whey protein
(%)16.8 15.7 17.0 58.2 48.0 45
(Lisozime, ppm) - trace trace 500 4000 700
Non proteic
nitrogen4.66 8.7 – 7.13
5 –
7.4311.4 14.0 10
Average nitrogen composition of the milk in some species
Cow+ Goat+ Horse*Donkey*
*Woman***
β lactoglobuline
(% of total
wheyprotein)
64 56 60 30 No
α lactoalbumine
(% of total
wheyprotein)
28 47 2-15 22 High
αs casein (% of casein) 450 – 5(as1)
12,6(as2)Yes Yes no
K casein (% of casein) 12 8.1 ? ?
γ casein (% of casein) 2.5 3.9 Yes Yes
β casein (% of casein) 30-35 75 Yes Yes
Some Milk Proteic Fractions
+ Jenness, 1980; *Doreau et Boulot, 1989; **Buscemi, 2000;***Ambruzzi, 2003
More investigations are necessary!
Average value
Total saturated % 67.58
volatile, soluble saturated %
0.91
Total Monounsaturated% 15.82
Total Polyunsaturated % 16.60
Polyunsaturated ω3 % 7.45
Polyunsaturated ω 6 % 8.65
Polynsaturated ω 3/ ω 6 0.86
Acidic composition of the donkey milk fat(Chiofalo 2001)
Essential Fatty Acids (EFAs) and human healthEssential Fatty Acids (EFAs) and human health
EFAsEFAs are long-chain polyunsaturated fatty acids derived from are long-chain polyunsaturated fatty acids derived from linolenic (linolenic (Omega-3Omega-3), linoleic (), linoleic (Omega-6Omega-6), and oleic acids ), and oleic acids
((Omega-9Omega-9)) (The number following "Omega-" represents the position of the first double bond,
counting from the terminal methyl group on the molecule)
Omega-9Omega-9 is "non-essential" because the body can manufacture a
modest amount on its own, provided essential EFAs are present
EFAs EFAs are necessary fats that humans cannot synthesize, and are necessary fats that humans cannot synthesize, and must be obtained through diet and support the cardiovascular, must be obtained through diet and support the cardiovascular, reproductive, immune, and nervous systems by the production reproductive, immune, and nervous systems by the production of prostaglandinesof prostaglandines (they are fundamental for growth in fetuses (by (they are fundamental for growth in fetuses (by mother’s dietary intake) and children, particularly for neural mother’s dietary intake) and children, particularly for neural development and maturation of sensory systemdevelopment and maturation of sensory system (www.goodfats.pamrotella.com(www.goodfats.pamrotella.com))
Essential Fatty Acids (EFAs) and human healthEssential Fatty Acids (EFAs) and human healthFoods that contain omega 3:Foods that contain omega 3: Foods that contain omega 6:Foods that contain omega 6:
Flaxseed oil (flaxseed oil has the highest linolenic content of any food), flaxseeds and flaxseed meal
Flaxseed oil, flaxseeds and flaxseed meal
hempseed oil and hempseeds hempseed oil and hempseeds
walnuts Grapeseed oilpumpkin seeds pumpkin seedsBrazil nuts pine nuts
sesame seeds pistachio nutsAvocados sunflower seeds (raw)some dark leafy green vegetables olive oil, olives, borage oilcanola oil (cold-pressed and unrefined)
evening primrose oil
soybean oil black currant seed oilwheat germ oil chestnut oilFish chicken
Corn, safflower, sunflower, soybean, and cottonseed oils are also sources of linoleic acid, but are refined and may be nutrient-deficient as sold in stores.
Conjugated linoleic acids (CLA) and human healthConjugated linoleic acids (CLA) and human health
Mixture of positional and geometric isomers of linoleic acids Mixture of positional and geometric isomers of linoleic acids with two double bonds, which can be different in the position with two double bonds, which can be different in the position and orientation (and orientation (ciscis or or transtrans) located on adjacent carbons.) located on adjacent carbons.
The main source of CLA in human diets:The main source of CLA in human diets:
-food products derived from ruminants (meat and dairy -food products derived from ruminants (meat and dairy products) products)
The CLA are showingThe CLA are showing impressive range of beneficial health impressive range of beneficial health effects in biomedical studies with animal models, like:effects in biomedical studies with animal models, like:
Anti-carcinogenic Anti-carcinogenic Anti-obesity and altered nutrient partitioning Anti-obesity and altered nutrient partitioning Anti-atherogenic and reduces cholesterol Anti-atherogenic and reduces cholesterol Enhance the immune system Enhance the immune system Enhance bone mineralizationEnhance bone mineralization
(Bauman & Lock, 2006)
Conjugated linoleic acids (CLA)Conjugated linoleic acids (CLA)cis-9, trans-11 CLA and trans-10, cis-12 CLA
cis-9, trans-11 conjugated linoleic acid is the predominant CLA isomer found in ruminant fat including milk fat.
CLA is produced as an intermediate in rumen biohydrogenation of linoleic acid present in the cows diet and a portion of the CLA in milk fat comes from that which has escaped complete biohydrogenation in the rumen.
However, the majority is made by the cow herself using the desaturase enzyme and trans-11 18:1 (vaccenic acid), another fatty acid intermediate produced during rumen biohydrogenation.
With certain diets the rumen environment is changed and a portion of the biohydrogenation produces, trans-10, cis-12 CLA and trans-10 C18:1 as intermediates. trans-10, cis-12 CLA is present at only trace levels in milk fat (Bauman & Lock, 2006)
cis-9, trans-11 CLA
Trans vaccenic and CLA content in milk fat of different species in comparison with human milk fat
(Jahreis et al., 1999)
Collaborative Research projectCollaborative Research project: effect of different diet and oxytocin use on donkey milk fat
yield and fatty acids composition
INVESTIGATORS: INVESTIGATORS: Cornell University Department of Animal Science (USA) ,
Istituto Sperimentale Zootecnico per la Sicilia and University of Palermo Department SEn. Fi.Mi.Zo. (Italy)
OBJECTIVES:OBJECTIVES:– To investigate changes in the pattern of donkey
milk fatty acids, using two different diets, one of them with an extra virgin olive oil integration.
– To investigate changes in donkey milk fat yield and fatty acids composition, using oxytocin
Researchable problem
The donkey as livestock animal
MILKMILK
Medicine:
- PEDIATRICS- PEDIATRICS
- GERIATRICS- GERIATRICS
- CARDIOLOGICS- CARDIOLOGICS
- FOR SOME TUMORS- FOR SOME TUMORS
Cosmetic industry Cosmetic industry
Problem articulation
Researchable problem
The donkey as livestock animal
MEATMEAT
- brasato- brasato- StracottoStracotto
- Smoked meat - Smoked meat -Cold meat and Cold meat and -salamisalami
Problem articulation
Researchable problemThe EU is showing a particular attention to promote the livestock production in marginal areas, to prevent and to stop the abandon of these rural areas, which can cause very relevant and negative consequences on the environment, the economical and social aspect.
The donkey is a breed in extinction in developed countries, and this animal is presented in marginal areas, thanks its high resistance and adaptation capacity
Problem articulation
Donkey breeds in Italy
6 main donkey breeds
Martina Franca Sardo
Donkey breeds in Italy
6 main donkey breeds
Amiata Asinara
Romagnolo
Donkey breeds in Italy
6 main donkey breeds
Ragusano Pantesco
Sicilian
The donkey, which has used in the past to work
the land and to transport, now as a livestock animal can be an
alternative profitability resource for the
marginal areas of the Mediterranean as well as
for many agriculture areas of the developing countries, In relationship with the other income by the sale of donkey for meat and by other possible utilizations
(trekking, brain gym, pet therapy)
Problem of Donkey milk sale in Italy
• The donkey milk is not included in the DPR 54/97
• The sale can be only direct (from the farmer to the consumer) through autorization from the ASL (local sanitary agency) (Regio Decreto 9Regio Decreto 9thth May 1929 n° 994) May 1929 n° 994)
• The VETERINARY INSPECTORATEThe VETERINARY INSPECTORATE has institued in Sicily a
TECHNICAL AND SCIENTIFIC COMMITTEE TECHNICAL AND SCIENTIFIC COMMITTEE to investigate more to investigate more
donkey milk production and health aspects, to establish donkey milk production and health aspects, to establish
policies and marketing strategies improving this new policies and marketing strategies improving this new
productive sectorproductive sector
Dynamic problem definition Define a “Reference Mode”
Graphs or other information showing the development of the relevant problem over time
The information about the donkey milk production and
donkey milk consume is not registered by the statistical institutions in Italy;
The donkey indeed is not included into animals producing milk but only meat, considering the low incidence of this product and the actual selling and production restrictions.
Problem articulation
In Italy, since 1960, donkey number has decreased In Italy, since 1960, donkey number has decreased highly (FAO 2005).highly (FAO 2005).
050,000
100,000150,000200,000250,000300,000350,000400,000450,000500,000550,000
Time (Year)
Donk
ey (h
eads
)
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Time (Year)
Mil
k p
rod
uct
ion
rat
e (L
iter
/yea
rs)
Qualitative reference mode of donkey milk Qualitative reference mode of donkey milk production (liter/month)production (liter/month)
Qualitative future reference mode of donkey milk Qualitative future reference mode of donkey milk production (liter/month) production (liter/month)
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
Time (Year)
Mil
k p
rod
uct
ion
rat
e (L
iter
/yea
rs)
It is important to define the relevant time horizonenough of past to show problem development
enough into future to show possible delayed effects of potential policies
In this case I have chosen 8 years like time horizon, because I think that this can be enough to evaluate the results of application of different policies and strategies, increasing the milk production and then the milk available for the market
Problem articulation Dynamic problem definition
Dynamic Hypothesis
• A proposed explanation for the A proposed explanation for the behaviorbehavior– Explicit structure that creates behavior– Provisional, subject to revision (working
theory)
• Endogenous focusEndogenous focus– Behavior arising from within the system– Not (just) external shocks
• Avoid “narrow” model boundaries
Tools for Dynamic Hypotheses
• Model Boundary Chart– What variables included, excluded– What variables exogenous, endogenous
• Causal Loop Diagrams (CLD)– Show causal linkages among variables– Focus on feedback structure
• Stock-Flow Diagrams (SFD)– Characterize physical stock-flow structure
Dynamic Hypothesis
Population and birth rate loop
PopulationBirth Rate
++Births
INCREASE in population increases births, INCREASE in births increases population.
This is a POSITIVE feedback loop, which will cause population to grow.
Dynamic Hypothesis
Population and death rate loop
PopulationDeath Rate
+-Deaths
Population INCREASES death rate, death rate DECREASES population
This is a NEGATIVE or BALANCING feedback loop
Dynamic Hypothesis
PopulationDeath RateBirth Rate
++-+Births
Deaths
The simple system has two feedback loops
These operate together to produce the behavior of the system.
Population increases birth rate, birth rate increases population
Population increases death rate, death rate decreases population
Dynamic Hypothesis
Dynamic HypothesisBS sales and
death rate
Breedingstock
-
+
B
BS sale anddeath
Dynamic Hypothesis
BS sales anddeath rate
Breedingstock
-
+
B
BS sale anddeath
Birth rate
+
BS sales anddeath rate
Breedingstock
-
+
Young stock
B
BS sale anddeath
Birth rate
+
+
Dynamic Hypothesis
BS sales anddeath rate
Breedingstock
-
+
Young stock
B
BS sale anddeath
Birth rate
+
+
Dynamic Hypothesis
Young stock
YS sales anddeath rate
+
YS sales anddeath rate
Maturationrate
-
BS sales anddeath rate
Breedingstock
-
+
Young stock
B
BS sale anddeath
Birth rate
+
+
Young stock
YS sales anddeath rate
+
Dynamic Hypothesis
BS sales anddeath rate
Breedingstock
-
+
Young stock
B
BS sale anddeath
Birth rate
+
+
Young stock
YS sales anddeath rate
+
YS sales anddeath rate
Maturationrate
-
Dynamic Hypothesis
Breeding stock
+
Maturationrate
+
YS sales anddeath
B
YS sales anddeath rate
Maturationrate
-
BS sales anddeath rate
Breedingstock
-
+
Young stock
B
BS sale anddeath
Birth rate
+
+
Young stock
YS sales anddeath rate
+
Dynamic Hypothesis
Breeding stock
+
Maturationrate
+
YS sales anddeath
B
R
YS decrease
Young stock++
Maturationrate
-
+
-
++
-+
Breedingstock
Donkey milkproduction
+
Milk productionper donkey
+
Breedingstock
Donkey milkproduction
+
Milk productionper donkey
+Milk available for
the market+
Donkey milkper YS
-
Milk consume peryoung donkey
+
Youngstock
+
Milk available forthe market
Ratio of demand tomilk available for the
market
-Donkey milk
price+
Revenuemilk
++B
Production, demand,price, net margin of
milk
Breedingstock
Donkey milkproduction
+
Milk productionper donkey
+Milk available for
the market+
Donkey milkper YS
-
Milk consume peryoung donkey
+
Youngstock
+
Milk available forthe market
Ratio of demand tomilk available for the
market
-Donkey milk
price+
Donkey milkdemand
+
-
Revenuemilk
++
BDonkey milk
demand
B
Production, demand,price, net margin of
milkConsumer
+
+
Breedingstock
Donkey milkproduction
+
Milk productionper donkey
+Milk available for
the market+
Donkey milkper YS
-
Milk consume peryoung donkey
+
Youngstock
+
BS sales anddeath rate
Breedingstock
Birth rate
+
+
Donkey milkproduction
+
Milk productionper donkey
+
Milk available forthe market
+
Donkey milkper YS
-
Milk consume peryoung donkey
+Young stock+
+
YS sales anddeath rate
Ratio of demand to milkavailable for the market
-
Donkey milkprice
+
Donkey milkdemand
+
-
Revenuemilk
++
Revenuedonkey sale
Totalrevenue
Donkey price
++ +Maturation
rate
-
B
BS sale and death
B
Donkey milk demand
B
Production, demand,price, net margin of
milk Consumer
+
R
YS sales anddeath
+
-
-
+
B
+
YS decrease
++
Net margin forfarmers
Total donkey (YSand BS)
BS sales anddeath rate
Breedingstock
Birth rate
+
+
Donkey milkproduction
+
Milk productionper donkey
+
Milk available forthe market
+
Donkey milkper YS
-
Milk consume peryoung donkey
+Young stock+
+
YS sales anddeath rate
Ratio of demand to milkavailable for the market
-
Donkey milkprice
+
Donkey milkdemand
+
-
Revenuemilk
++
Revenuedonkey sale
Totalrevenue
Donkey price
++ +
+
Total costs
-
Other costs
+
Feed cost+ +
+
Feed resourceper land
-
Maturationrate
-
B
BS sale and death
B
Donkey milk demand
B
Production, demand,price, net margin of
milk Consumer
+
R
YS sales anddeath
+
-
-
+
B
+
-
YS decrease
B
Cost
++
Net margin forfarmers
Total donkey (YSand BS)
BS sales anddeath rate
Breedingstock
Birth rate
+
+Donkey milkproduction
+
Milk productionper donkey
+
Milk available forthe market
+
Donkey milkper YS
-
Milk consume peryoung donkey
+Young stock+
+
YS sales anddeath rate
Ratio of demand to milkavailable for the market
-
Donkey milkprice
+
Donkey milkdemand
+
-
Revenuemilk
++
Revenuedonkey sale
Totalrevenue
Donkey price
+++
+
Total costs
-
Other costs
+
Feed cost+ +
+
Feed resourceper land
-
Maturationrate
-
B
BS sale and death
B
Donkey milk demand
B
Production, demand,price, net margin of
milk Consumer
+
R
YS sales anddeath
+
-
-
+
B
+
-
YS decrease
B
Cost
++
DesiredBreeding stock
+
- -
R
Effect of desiredbreeding stock on Net
margin
Explicit delineation of stocks and flows
Stocks (states) are accumulations– Material or information– Can be counted at a given time– Change only through flows
Flows (rates) are quantities per some amount of time- Change states- Cannot be measured instantaneously- Can be affected by many other variables
Dynamic Hypothesis
• “State-rate” structure
Rates affect states, states affect ratesRates affect states, states affect rates
• Units consistency for variables, equations– Explicit specification of units for each
element
Dynamic Hypothesis
Graphical Representation
PopulationBirthRate
DeathRate
StockFlow Flow
Dynamic Hypothesis
Graphical Representation
FBR and AL are Auxiliary Variables (neither stocks nor flows)
PopulationBirthRate
DeathRate
StockFlow Flow
Stock
Fractional BirthRate
AverageLifespan
Population also determines the birth and death rates in this case
Dynamic Hypothesis
Stock and flows diagrams AGING CHAIN DONKEYS
YoungStock
BreedingStockMaturation
rateBirth rate
YS Sales rate
YS Death rate
BS Sales rate
BS Death rate
Exports
ImportsFoaling Interval
Maturation Delay
BS Mortality Rate
BS AverageLifetime
YS MortalityFraction
YS Sales Fraction
<MaturationDelay>
Effect of desiredbreeding stock on YS
sales fraction
Effect of desired breedingstock on BS average
lifetime
Effect of desired breedingstock on YS mortality
fraction
Effect of desiredbreeding stock on BS
mortality rate
Stock and flows diagrams
MILK PRODUCTION, MILK DEMAND AND MILK PRICE
Donkey MilkDemand
Donkey MilkPrice
Reference DonkeyMilk Price
Reference DonkeyMilk Demand
Sensitivity ofDemand to Price
Ratio of Demand to MilkAvailable for Market
Sensitivity of Priceto the Ratio
Smooth of Ratio
Smooth Time
Milk productionper donkey
Milk consume peryoung donkey
Total MilkProduction
Milk Consumedby YS Milk Available for
the Market
Breedingstock
Youngstock
Initial sales perconsumers
Initial demand
Adoptionrate
Repeated demand
Consumer Averageconsumption per
consumer
Stock and flows diagrams CONSUMER
Potentialconsumers
Consumers
Adoption rate
Adoption fromdoctor's advertising
+
Advertisingeffectiveness
Adoption fromword of mouth
++ Total population
Adoption fraction
Contact rate
-+
Discard rate
Average productuse
-
+
C/TP
Stock and flows diagrams FEED RESOURCE
Feedresourcesper land
Growth rate Consumptionrate
Land area
Relative Rainfall
+
Fractional growthrate
Carrying capacity
FRPL to CCRatio Stocking rate
Minimum feed onland time
Feed Per DonkeyPer Month
Losses rate
Proportional losses
Pulse height1
Reference RainfallAverage Land Area
Per Farm
Effect of Rainfallon CC
Observed Rainfall
Effect of FRPL toCC Ratio on FGR
Total donkeys
Number offarm
Youngstock
Breedingstock
Purchased feed
Max FeedConsumption Rate from
FRPL
Stock and flows diagrams DONKEY FARM SYSTEM
Number ofFarms Exit RateEntry Rate
Reference NetMargin
Lookup Functionfor Entry
Reference ExitRate
Lookup Functionfor Exit
Minimum Time inFarm
Donkey per farm
Entrance Delay
Farm carryingcapacity
Number of farmsto FCC
Fractional entryrate
Effect of number offarms to FER
Breedingstock
Smooth of actualnet margin
Donkey MeatPrice
Donkey Price
Carcass YieldMarketing Costs
Total revenue
Total costs
Non feed unit variablecost per donkey
Milk Availablefor the Market
Actual NetMargin
Purchasedfeed
Feed price
Feed costs
Desired BreedingStock
Relative net margin
Effect of Net Margin ondesired Breeding stock
Smooth of ActualNet Margin
NM smooth time
Milk Revenues
Meat Revenues
Breedingstock
Youngstock Donkey milk
price
Reference netmargin
YS salesrate
BS salesrate
Stock and flows diagrams REVENUE, COSTS AND NET MARGIN
Mathematics of SD models
• System of ordinary differential equations• Solved by numerical integration
– St = ∫(Inflow-Outflow) ds + S0
– Inflow = f(S, other variables)
– Outflow = f(S, other variables)
• Many software programs available
– Vensim® is good for research purposes
Dynamic Hypothesis
Testing (Formulate Simulation Model)
• An explicit mathematical representationhelps to:
– Identify vague concepts– Resolve contradictions previously unnoticed
• Provides a real test of understanding of the problem and its elements– Specification must be complete and
consistent• Many softwares available
– I’m using Vensim® from Ventana Systems
Model Evaluation
• Begins with first equation• Comparison of behavior in model to real world• Concepts in model should correspond to a
meaningful real world concept• Check for
– Dimensional consistency– Sensitivity to parameter changes– Response to “extreme conditions”
Policy Design and Evaluation
• Modifications to relevant parameters• Creation of entirely new strategies and structures
– Modifying the feedback structure– Reducing or eliminating the delays– Changing information flows– Altering decision rules
• Assess sensitivity of policy results
And now a little bit of practice!
Milk production model
Thank you !