Difference between revisions of "Impact of late lockdown and hospital capacity on 19-COVID spread"

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(Created page with "Introduction =Problem definition= =Method= =Model= =Results= =Conclusion= =Code= =Problem definition= Crop rotation is based on growing a series of different types of c...")
 
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Vensim modelling approach was selected due to dynamic behavior of the simulated system.
 
Vensim modelling approach was selected due to dynamic behavior of the simulated system.
 +
 +
Our model shows the evolution of the number of people who got infected by the covid-19 virus in
 +
countries where the population have a high health care protection and where the government has
 +
decided to make a decision about the virus propagation. The model will explain us how fast the
 +
virus spreads itself and how political decisions can change the number of infected people at a
 +
given point in time. The construction of our model is simple: at the beginning one person is
 +
infected and will, then, infect other people. When you are infected many possibilities are open:
 +
- You can be a healthy virus carrier. In that case you will not know that you have the virus and
 +
after 19 days you will be recovered.
 +
- After 5 days you get symptoms, but you do not have any important health problems, you will
 +
recover in the next 14 days.
 +
- After 5 days, you get symptoms and you are very sick and have respiratory problems, after 5
 +
more days, still feeling bad, you decide to go to the doctor to get a diagnosis. The doctor has than
 +
two options: to send you back home, where you will recover after 9 more days or hospitalized you
 +
the next day.
 +
- The doctor had decided to hospitalize you, you then spend two weeks in intensive care, where
 +
you will need artificial ventilation and 24-hour nursing care. You can then recover or unfortunately
 +
die. The higher the hospital utilisation, the greater the probability to die. Indeed, if hospitals get
 +
busier, the medical staff will be less available and health care quality might decrease.
 +
When the number of diagnosed and hospitalised people (which are the official number of cases)
 +
is greater than 3% of the population, the government will take decisions to slow down the spread
 +
of the virus. This is to say that governments will take some measures, such as installing a forced
 +
lockdown of the population to decrease the number of contacts per habitant. Moreover, such a
 +
decision will have an impact of the population, which will be more aware of the situation and will
 +
be more careful to not spread or contract the virus. So, the contagion rate as the number of contacts
 +
per people will slow down. They will be less hospitalised people, that means less people in the
 +
hospital, meaning a better service and so less people who die.
  
 
=Model=
 
=Model=
  
 
Following vensim model was developed based on the study.
 
Following vensim model was developed based on the study.
−
[[File:Crop_rotation_vensim_finished.png|900px|thumb|center|Crop rotation Stock Flow Diagram]]
+
 
 +
[[File:Model1.png|900px|thumb|center|COVID-19 pandemic in Switzerland Stock Flow Diagram]]
  
 
== Variables ==
 
== Variables ==
  
−
===Number of rotated crops===
+
===Initial population===  
−
''Input constant variable which can be changed based on crop strategy. Range <1,3>.''
+
The Population of Switzerland can be found here: https://en.wikipedia.org/wiki/Demographics_of_Switzerland
 +
=8 500 000
  
−
=1 (in case of CCC and SSS strategies)
+
===Ratio susceptible people===
 +
Decrease the susceptible people by the number of deaths due to the COVID-19.
  
−
=2 (in case of CS strategy)
+
=Susceptible population / (Initial population-Dead)
  
−
=3 (in case of CSW strategy)
+
===Susceptible population===
 +
Deacrease the susceptible population by the number of new infections.
  
−
===Corn production===
+
=-New infections
−
''PULSE TRAIN function ensures specific crop to be delivered by time pattern. In crop rotation strategies, if corn is present, it is always first crop, therefore it starts at time 0 (first year), duration is 1 year, repetition is based on number of crops (eg. 2 - it repeats in third year) and final time is set to fixed 40 years according to simulation setup.''
 
  
−
=Corn quantity*PULSE TRAIN(0, 1 , Number of rotated crops ,40)
+
===contact===
 +
'''Lockdown:''' ''Function IF THEN ELSE that bring the number of new people you meet (contact) from 5 to 0.05 if the ratio exceed 0.015%''
 +
=IF THEN ELSE("Ratio official cases/pop"> 0.00015, 0.05 , 5 )
  
−
===Soybean production===
+
===Contagion Rate===  
−
''Similar case as in Corn production variable only with difference of initial year, if sobyean is present in crop rotation strategy, it is always second crop, therefore it starts at time 1 (second year). In case of SSS strategy it starts at time 0 (first year)''
+
'''Lockdown:''' ''contagion rate: IF THEN ELSE that bring the contagion rate from 0.1 to 0.075 if the ratio exceed 0.015%. Function of the awareness in the population (washing more often their hands if lockdown)''
  
−
=Soybean quantity*PULSE TRAIN(IF THEN ELSE(Number of rotated crops=1, 0 , 1 ), 1 , Number of rotated crops , 40 )
+
=IF THEN ELSE("Ratio official cases/pop" > 0.00015 , 0.075 , 0.1 )
  
−
===Wheat production===
+
===New infections===  
−
''Similar case as in Corn production variable only with difference of initial year, if wheat is present in crop rotation strategy, it is always third crop, therefore it starts at time 2 (third year).''
+
It represent the new number of people infected each day.
  
−
=Wheat quantity*PULSE TRAIN(2, 1 , Number of rotated crops , 40 )
+
=contact*Infected*Ratio susceptible people*Contagion Rate
  
−
===Corn quantity===
+
===Infected===  
−
''Input constant variable which can be changed based on crop strategy. Range <0,100>.''
+
It represent the stock of the new people infected. We start the model with 1 infected people.
  
−
=100 (in case of CCC, CS and CSW strategies)
+
=New infections-New recoveries from infected-New symptoms ///// Initial value: 1
  
−
=0 (in case of SSS strategy)
+
===New recoveries from infected===
 +
It represent the number of people without symptoms and they go in the recovered stock after 19 days.
  
−
===Soybean quantity===
+
=((1-Ratio Symptoms)*Infected)/TTA recoveries
−
''Input constant variable which can be changed based on crop strategy. Range <0,40>.''
 
  
−
=40 (in case of CS, SSS and CSW strategies)
+
===New symptoms===
 +
It represent the number of new people that develop symptoms during 5 days before going to the symptomatic stock.
  
−
=0 (in case of CCC strategy)
+
=(Infected*Ratio Symptoms)/TTA Symptoms
  
−
===Wheat quantity===
+
===Ratio Symptoms===  
−
''Input constant variable which can be changed based on crop strategy. Range <0,80>.''
+
The ratio of people who develop some symptoms.
  
−
=80 (in case of CSW strategy)
+
=0.2
−
 
 
−
=0 (in case of CCC, CS, SSS strategies)
 
−
 
 
−
===increase of N2O emissions===
 
−
''Auxiliary variable which changes based on each crop production and its emission coefficient (pattern was extracted from a study),additionaly it changes slightly according to tillage strategy and usage of extra inorganic fertilizer.''
 
  
−
Corn production*Corn N2O emission coef*IF THEN ELSE(Tillage=0, 1.5 , 1.4)+
+
===Symptomatic===
−
Soybean production*Soybean N2O emission coef*IF THEN ELSE(Tillage=0, 1.5 , 1.4)+
+
The stock of people symptomatic.
−
Wheat production*Wheat N2O emission coef*IF THEN ELSE(Tillage=0, 1.5 , 1.4)+
 
−
Inorganic fertilizer/5
 
  
−
===increase of CO2 emissions===
+
=New symptoms-New Diagnostic-New recoveries from symptoms
−
''Similar auxiliary variable as increase of N20 emissions extra except zero effect with inorganic fertilizer.''
 
  
−
Corn production*Corn CO2 emission coef*IF THEN ELSE(Tillage=0, 3 , 2.9)+Soybean production*Soybean CO2 emission coef*IF THEN ELSE(Tillage=0, 3 , 2.9)+Wheat production
+
===New recoveries from symptoms===
−
*Wheat CO2 emission coef*IF THEN ELSE(Tillage=0, 3 , 2.9)
+
It represent the number of people that have symptoms but that were not to the doctor, after 14 they are in the recovered stock.
  
−
===increase of CH4 emissions===
+
=(1-Ratio Diagnost)*Symptomatic/TTA sympt
−
''Similar auxiliary variable as increase of N20 emissions extra except zero effect with inorganic fertilizer.''
 
  
−
Corn production*Corn CH4 emission coef*IF THEN ELSE(Tillage=0, 0.3 , 0.2)+Soybean production*Soybean CH4 emission coef*IF THEN ELSE(Tillage=0, 0.3 , 0.2)+Wheat production
+
===New Diagnostic===
−
*Wheat CH4 emission coef*IF THEN ELSE(Tillage=0, 0.3 , 0.2)
+
It represent the number of new people that are diagnosed each day, they stay 3 days before having the diagnostic.
  
−
===Cumulative N2O emissions===
+
=Symptomatic*Ratio Diagnost/TTA Diagnostic
  
−
=INTEG(increase of N2O emissions)
+
===Ratio Diagnost===
 +
the ratio of people that are diagnosed by a doctor.
  
−
===Cumulative CO2 emissions===
+
=0.4
  
−
=INTEG(increase of CO2 emissions)
+
===Diagnosed===
 +
the stock of people that are diagnosed by a doctor
  
−
===Cumulative CH4 emissions===
+
=New Diagnostic-New Hospitalization-New Recovery from Diagnostic
  
−
=INTEG(increase of CH4 emissions)
+
===New Recovery from Diagnostic===
 +
It represent the number of people that are diagnosed and not sent to the hospital. they stay 9 days before going in the Recovered stock.
  
−
===Corn N2O emission coef===
+
=(1-Ratio Hospitalize)*Diagnosed/TTA diag
−
''Constant variable extracted from study and its data, representing how much N20 emissions is produced while producing corn. Can be adjusted in the future. Further coefficients are similar just crop and emission type changes.''
 
  
−
=0.5
+
===New Hospitalization===
 +
It represent the number of people that are diagnosed and sent to the hospital. they stay 1 day before being sent.
  
−
===Soybean N2O emission coef===
+
=(Diagnosed*Ratio Hospitalize)/TTA Hospitalization Ratio Hospitalize
  
−
=0.07
+
===Ratio Hospitalize===
−
 
+
the ratio of people that are Hospitalized.
−
===Wheat N2O emission coef===
 
−
 
 
−
=0.005
 
−
 
 
−
===Corn CO2 emission coef===
 
  
 
=0.3
 
=0.3
  
−
===Soybean CO2 emission coef===
+
===Hospitalized===  
 +
The stock of people that are hospitalized before being Recovered or Dead.
  
−
=0.2
+
=New Hospitalization-New Death-New Recovery
  
−
===Wheat CO2 emission coef===
+
===New Recovery===  
 +
The number of people that recover each day from the hospitalization. It depends from the Mortality Rate which is depend from the hospital utilization (capacity or number of beds...).
  
−
=0.25
+
=((1-Mortality Rate)*Hospitalized)/TTA Hospitalization
  
−
===Corn CH4 emission coef===
+
===New Death===
 +
The number of people that die each day from the hospitalization. It depends from the Mortality Rate which is depend from the hospital utilization (capacity or number of beds...).
  
−
=0.005
+
=(Hospitalized*Mortality Rate)/TTA Hospitalization
  
−
===Soybean CH4 emission coef===
+
===hospital bed===  
 +
The number of bed in Switzerland 356 beds for 100k people with 8.5M people it gives that number: https://www.swissinfo.ch/eng/coronavirus-crisis-_has-switzerland-got-enough-hospital-beds--/45671704
  
−
=0.01
+
=30260
  
−
===Wheat CH4 emission coef===
 
  
−
=0.05
+
===hospital utilization===
 +
The utilization of the hospital we assume that there is already 8260 beds used for patient outside the covid pandemic. The more the hospital is used (from 0 to 100%) the more the greater the probability to die. Indeed, if hospitals get
 +
busier, the medical staff will be less available and health care quality might decrease
 +
=(Hospitalized/(hospital bed - 8260))
  
−
===Inorganic fertilizer===
+
===lk mortality===
−
''Auxiliary variable observes if soil nitrogens decrease below 20 and applies 30 units of fertilizer (which affects NO2 emissions)''
 
  
−
=IF THEN ELSE(Soil nitrogen<20, 30 , 0 )
+
=[(0,0)-(1000,0.3)],(0,0.05),(0.8,0.05),(0.9,0.1),(1,0.15),(1.2,0.2),(10,0.2),(1000,0.2)
  
−
===Soil nitrogen===
+
===Mortality Rate===  
−
''Level variable observes how much nitrogen in soil increase or decrease''
 
  
−
=INTEG(increase of soil N-decrease of soil N)
+
=lk mortality (hospital utilization)
  
−
===increase of soil N===
+
===Dead===  
−
''Increase of nitrogen level in soil in this model is triggered either by application of inorganic fertilizer or soybean production (soybean and other legumes increase nitrogen level in soil by its decomposition - extracted from study and its data)''
 
  
−
=Inorganic fertilizer+Soybean production/12
+
=New Death
  
−
===decrease of soil N===
+
===Recovered===  
−
''Decrease of nitrogen level in soil in this model is triggered either by corn or wheat production (corn and wheat decreasing nitrogen level in soil by its farming - extracted from study and its data)''
 
  
−
=Corn production/8+Wheat production/12
+
=New recoveries from infected+New recoveries from symptoms+New Recovery+New Recovery from Diagnostic
  
−
===Soil nitrogen level===
+
===official active cases===
−
''Auxiliary variable which is dependent on soil nitrogen quantity. According to soil nitrogen levels, it affects yields based on random function with normal distribution. The less soil nitrogen level, the higher negative impact on yields.''
 
  
−
IF THEN ELSE(Soil nitrogen<10, RANDOM NORMAL( 0.3 , 0.5 , 0.45 , 0.05 , 100) , 1 )*
+
=Diagnosed+Hosptitalized
−
IF THEN ELSE(((Soil nitrogen>=10) :OR: (Soil nitrogen>20)), RANDOM NORMAL( 0.4 , 0.6 , 0.55 , 0.05 , 100) , 1 )*
 
−
IF THEN ELSE(((Soil nitrogen>=20) :AND: (Soil nitrogen<30)), RANDOM NORMAL( 0.5 , 0.8 , 0.75 , 0.05 , 100) , 1 )*
 
−
IF THEN ELSE(((Soil nitrogen>=30) :AND: (Soil nitrogen<40)), RANDOM NORMAL( 0.7 , 0.9 , 0.85 , 0.05 , 100) , 1 )*
 
−
IF THEN ELSE(Soil nitrogen>=40, RANDOM NORMAL( 1 , 1.4 , 1.2 , 0.05 , 100) , 1 )
 
  
−
===Temperature===
+
===Ratio official cases/pop===  
−
''Auxiliary variable which is based on random function with normal distribution. Study showed that extremes in temperatures can have impacts on harvests and yields. Due to complex behavior, I decided to implement only random function.''
 
  
−
=RANDOM NORMAL( 0.9 , 1.1 , 1 , 0.05 , 100)
+
=official active cases/ Initial population
  
−
===Precipitation===
+
===New Diagnosed.1===  
−
''Auxiliary variable, similar to Temperature variable only with different min and max parameters''
 
  
−
=RANDOM NORMAL( 0.8 , 1.2 , 1 , 0.05 , 100)
+
=New Diagnostic
  
−
===Pests===
+
===Total Diagnosed===  
−
''Auxiliary variable, similar to Temperature variable only with different min and max parameters''
 
  
−
=RANDOM NORMAL( 0.9 , 1 , 0.98 , 0.005 , 100)
+
=New Diagnosed.1
  
−
===Natural disasters===
+
===TTA recoveries===  
−
''Auxiliary variable, similar to Temperature variable only with different min and max parameters''
 
  
−
=RANDOM NORMAL( 0.01 , 1 , 0.99 , 0.05 , 100)
+
=19
  
−
===Tillage===
+
===TTA Symptoms===  
−
''Constant variable, which affects yield and emissions. 1 = tilled, 0 = no-till. No-till suppose to be better ecological decision according to study.''
 
  
−
=1 (can be changed to 0)
+
=5
  
−
===increase of C yield===
+
===TTA sympt===
−
''Corn yield calculated based on previous variables.''
 
  
−
=Corn production*Soil nitrogen level*IF THEN ELSE(Tillage=0, 1, 1.05)*Temperature*Precipitation*Pests*Natural disasters
+
=14
  
−
===increase of S yield===
+
===TTA Diagnostic===  
−
''Soybean  yield calculated based on previous variables.''
 
  
−
=Soybean production*Soil nitrogen level*IF THEN ELSE(Tillage=0, 1, 1.03)*Temperature*Precipitation*Pests*Natural disasters
+
=5
  
−
===increase of W yield===
+
===TTA diag===
−
''Wheat yield calculated based on previous variables.''
 
  
−
=Wheat production*Soil nitrogen level*IF THEN ELSE(Tillage=0, 1, 1.07)*Temperature*Precipitation*Pests*Natural disasters
+
=9
  
−
===Corn yield===
+
===TTA Hospitalization===  
  
−
=INTEG(increase of C yield)
+
=1
  
−
===Soybean yield===
+
===TTA Hospital===
  
−
=INTEG(increase of S yield)
+
=14
  
−
===Wheat yield===
+
===Number of rotated crops===
 +
''Input constant variable which can be changed based on crop strategy. Range <1,3>.''
  
−
=INTEG(increase of W yield)
+
=1 (in case of CCC and SSS strategies)
−
 
 
−
===Total yield===
 
−
''Sum of yields''
 
−
 
 
−
=Corn yield + Soybean yield + Wheat yield
 
  
 
=Results=
 
=Results=

Revision as of 16:28, 7 January 2021

Introduction

Problem definition

Method

Model

Results

Conclusion

Code

Problem definition

Crop rotation is based on growing a series of different types of crops in the same area in sequential seasons. The planned rotation may vary from a growing season to a few years or even longer periods. It is one of the most effective agricultural control strategies that is used in preventing the loss of soil fertility. It also helps in reducing soil erosion and increases crop yield. Planning an effective crop rotation requires weighing fixed and fluctuating production circumstances: market, farm size, labor supply, climate, soil type, growing practices, etc.

In this simulation I will try to find parameters which have impact on the whole process of crop rotation with goal to find model providing desired outputs (these were slightly changed from concept) - crop yields, greenhouse gas emissions (N2O, CO2, NH4), soil fertility (nitrogen levels).

I will focus on four crop rotation strategies with three different crops - corn, soybean, wheat:

CCC (continuous corn) - only corn will be farmed for the whole observed time period (40 years)

CS (corn-soybean) - rotation of corn and soybean will be used in year cycles for the whole observed time period (40 years), first year corn, second year soybean, repeat..

SSS (continuous soybean) - only soybean will be farmed for the whole observed time period (40 years)

CSW (corn-soybean-wheat) - rotation of corn, soybean and wheat will be used in year cycles for the whole observed time period (40 years), first year corn, second year soybean, third year wheat, repeat..

Goal of this simulation is to observe dynamic changes with yields, greenhouse gas emissions, tillage strategy and soil nitrogen levels, while changing different crop rotation strategies.

Method

Vensim modelling approach was selected due to dynamic behavior of the simulated system.

Our model shows the evolution of the number of people who got infected by the covid-19 virus in countries where the population have a high health care protection and where the government has decided to make a decision about the virus propagation. The model will explain us how fast the virus spreads itself and how political decisions can change the number of infected people at a given point in time. The construction of our model is simple: at the beginning one person is infected and will, then, infect other people. When you are infected many possibilities are open: - You can be a healthy virus carrier. In that case you will not know that you have the virus and after 19 days you will be recovered. - After 5 days you get symptoms, but you do not have any important health problems, you will recover in the next 14 days. - After 5 days, you get symptoms and you are very sick and have respiratory problems, after 5 more days, still feeling bad, you decide to go to the doctor to get a diagnosis. The doctor has than two options: to send you back home, where you will recover after 9 more days or hospitalized you the next day. - The doctor had decided to hospitalize you, you then spend two weeks in intensive care, where you will need artificial ventilation and 24-hour nursing care. You can then recover or unfortunately die. The higher the hospital utilisation, the greater the probability to die. Indeed, if hospitals get busier, the medical staff will be less available and health care quality might decrease. When the number of diagnosed and hospitalised people (which are the official number of cases) is greater than 3% of the population, the government will take decisions to slow down the spread of the virus. This is to say that governments will take some measures, such as installing a forced lockdown of the population to decrease the number of contacts per habitant. Moreover, such a decision will have an impact of the population, which will be more aware of the situation and will be more careful to not spread or contract the virus. So, the contagion rate as the number of contacts per people will slow down. They will be less hospitalised people, that means less people in the hospital, meaning a better service and so less people who die.

Model

Following vensim model was developed based on the study.

COVID-19 pandemic in Switzerland Stock Flow Diagram

Variables

Initial population

The Population of Switzerland can be found here: https://en.wikipedia.org/wiki/Demographics_of_Switzerland =8 500 000

Ratio susceptible people

Decrease the susceptible people by the number of deaths due to the COVID-19.

=Susceptible population / (Initial population-Dead)

Susceptible population

Deacrease the susceptible population by the number of new infections.

=-New infections

contact

Lockdown: Function IF THEN ELSE that bring the number of new people you meet (contact) from 5 to 0.05 if the ratio exceed 0.015% =IF THEN ELSE("Ratio official cases/pop"> 0.00015, 0.05 , 5 )

Contagion Rate

Lockdown: contagion rate: IF THEN ELSE that bring the contagion rate from 0.1 to 0.075 if the ratio exceed 0.015%. Function of the awareness in the population (washing more often their hands if lockdown)

=IF THEN ELSE("Ratio official cases/pop" > 0.00015 , 0.075 , 0.1 )

New infections

It represent the new number of people infected each day.

=contact*Infected*Ratio susceptible people*Contagion Rate

Infected

It represent the stock of the new people infected. We start the model with 1 infected people.

=New infections-New recoveries from infected-New symptoms ///// Initial value: 1

New recoveries from infected

It represent the number of people without symptoms and they go in the recovered stock after 19 days.

=((1-Ratio Symptoms)*Infected)/TTA recoveries

New symptoms

It represent the number of new people that develop symptoms during 5 days before going to the symptomatic stock.

=(Infected*Ratio Symptoms)/TTA Symptoms

Ratio Symptoms

The ratio of people who develop some symptoms.

=0.2

Symptomatic

The stock of people symptomatic.

=New symptoms-New Diagnostic-New recoveries from symptoms

New recoveries from symptoms

It represent the number of people that have symptoms but that were not to the doctor, after 14 they are in the recovered stock.

=(1-Ratio Diagnost)*Symptomatic/TTA sympt

New Diagnostic

It represent the number of new people that are diagnosed each day, they stay 3 days before having the diagnostic.

=Symptomatic*Ratio Diagnost/TTA Diagnostic

Ratio Diagnost

the ratio of people that are diagnosed by a doctor.

=0.4

Diagnosed

the stock of people that are diagnosed by a doctor

=New Diagnostic-New Hospitalization-New Recovery from Diagnostic

New Recovery from Diagnostic

It represent the number of people that are diagnosed and not sent to the hospital. they stay 9 days before going in the Recovered stock.

=(1-Ratio Hospitalize)*Diagnosed/TTA diag

New Hospitalization

It represent the number of people that are diagnosed and sent to the hospital. they stay 1 day before being sent.

=(Diagnosed*Ratio Hospitalize)/TTA Hospitalization Ratio Hospitalize

Ratio Hospitalize

the ratio of people that are Hospitalized.

=0.3

Hospitalized

The stock of people that are hospitalized before being Recovered or Dead.

=New Hospitalization-New Death-New Recovery

New Recovery

The number of people that recover each day from the hospitalization. It depends from the Mortality Rate which is depend from the hospital utilization (capacity or number of beds...).

=((1-Mortality Rate)*Hospitalized)/TTA Hospitalization

New Death

The number of people that die each day from the hospitalization. It depends from the Mortality Rate which is depend from the hospital utilization (capacity or number of beds...).

=(Hospitalized*Mortality Rate)/TTA Hospitalization

hospital bed

The number of bed in Switzerland 356 beds for 100k people with 8.5M people it gives that number: https://www.swissinfo.ch/eng/coronavirus-crisis-_has-switzerland-got-enough-hospital-beds--/45671704

=30260


hospital utilization

The utilization of the hospital we assume that there is already 8260 beds used for patient outside the covid pandemic. The more the hospital is used (from 0 to 100%) the more the greater the probability to die. Indeed, if hospitals get busier, the medical staff will be less available and health care quality might decrease =(Hospitalized/(hospital bed - 8260))

lk mortality

=[(0,0)-(1000,0.3)],(0,0.05),(0.8,0.05),(0.9,0.1),(1,0.15),(1.2,0.2),(10,0.2),(1000,0.2)

Mortality Rate

=lk mortality (hospital utilization)

Dead

=New Death

Recovered

=New recoveries from infected+New recoveries from symptoms+New Recovery+New Recovery from Diagnostic

official active cases

=Diagnosed+Hosptitalized

Ratio official cases/pop

=official active cases/ Initial population

New Diagnosed.1

=New Diagnostic

Total Diagnosed

=New Diagnosed.1

TTA recoveries

=19

TTA Symptoms

=5

TTA sympt

=14

TTA Diagnostic

=5

TTA diag

=9

TTA Hospitalization

=1

TTA Hospital

=14

Number of rotated crops

Input constant variable which can be changed based on crop strategy. Range <1,3>.

=1 (in case of CCC and SSS strategies)

Results

As explained in problem definition sections, four crop rotation strategy were observed: CCC, CS, SSS, CSW. For each strategy I changed necessary variables and did simulation run. I divided results into yields, N2O and CO2 emissions, CH4 emissions and soil nitrogen for comparsion between mentioned crop rotation strategies.

Yields

CCC Yield CCC Yield

CS Yield CS Yield

SSS Yield SSS Yield

CSW Yield CSW Yield


N2O and CO2 emissions

CCC N2O CO2 CCC N2O CO2

CS N2O CO2 CS N2O CO2

SSS N2O CO2 SSS N2O CO2

CSW N2O CO2 CSW N2O CO2

CH4 emissions

CCC CH4 CCC CH4

CS CH4 CS CH4

SSS CH4 SSS CH4

CSW CH4 CSW CH4

Soil nitrogen

CCC soil nitrogen CCC soil nitrogen

CS soil nitrogen CS soil nitrogen

SSS soil nitrogen SSS soil nitrogen

CSW soil nitrogen CSW soil nitrogen

Conclusion

Simulation of such complex evnironment as crop rotation in farming was challenging. Created VENSIM model of crop rotation is simplified with some parameters based on just few studies. Real world behaviour can be different because there are many variables affecting the whole process. Although with dramatic simplification, it can be used as a starting point for creating more complex models in agriculture sector. My goal to demonstrate changing of yield, greenhouse gas emissions and nitrogen level was achieved.

Yields results could be extended with price and demand of market implementation for comparsion with different crop yields.

N2O and CO2 emissions were highest in CCC crop strategy, providing speculation that monoculture is enviromentaly unfriendly in compare with polyculture strategies. SSS strategy was providing lowest N2O emissions, on other hand it showed highest CH4 emissions which are specific for legumes as soybean.

Soil nitrogen levels were highly dependent on soybean in crop rotation strategy. With no soybean in crop strategy - in CCC strategy, dramaticaly more inorganic fertilization inputs were necessary

Due to the scope of model, different tillage scenario (0 - no-til) was not simulated. Provided study concluded higher yield and lower emission levels with tillage sceario (1 - tillage, default for simulated model)

Model extension

More complexity can be implemented in the future, for example:

Detailed description of parameteres like Temperature, Pests, Natural disasters or Precipitation.

More crop strategies or new crops.

Market variables like demand or prices of crop seeds or harvesting costs could be also implemented.

Monthly changes (instead of yearly) with more detailed fluctuations during seasons (in Spring there is bigger demand for fertilizers, temperature spikes)

Code

Crop rotation VENSIM model

References

  1. BEHNKE, Gevan D., Stacy M. ZUBER, Cameron M. PITTELKOW, Emerson D. NAFZIGER a María B. VILLAMIL. Long-term crop rotation and tillage effects on soil greenhouse gas emissions and crop production in Illinois, USA. Agriculture, Ecosystems & Environment [online]. 2018, 261, 62-70 [cit. 2020-01-26]. DOI: 10.1016/j.agee.2018.03.007. ISSN 01678809. Availiable: https://linkinghub.elsevier.com/retrieve/pii/S0167880918301221
  2. KOLLAS, Chris, Kurt Christian KERSEBAUM, Claas NENDEL, et al. Crop rotation modelling—A European model intercomparison. European Journal of Agronomy [online]. 2015, 70, 98-111 [cit. 2020-01-26]. DOI: 10.1016/j.eja.2015.06.007. ISSN 11610301. Availiable: https://linkinghub.elsevier.com/retrieve/pii/S1161030115300010
  3. BRANKATSCHK, Gerhard a Matthias FINKBEINER. Modeling crop rotation in agricultural LCAs — Challenges and potential solutions. Agricultural Systems [online]. 2015, 138, 66-76 [cit. 2020-01-26]. DOI: 10.1016/j.agsy.2015.05.008. ISSN 0308521X. Availiable: https://linkinghub.elsevier.com/retrieve/pii/S0308521X1500075X
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