Working with Dynamic Models for Agriculture and Environment

Description

ZeBook Working with dynamic models for agriculture and environment (Working with Dynamic Crop Models)

Linked to book Working with Dynamic Crop Models (Elsevier), Third edition, 27 septembre 2018 by Wallach, Makowski, Jones and Brun. http://www.modelia.org/moodle/course/view.php?id=61

A full description of the models is in the book in appendix of the book.

Chapter numbers have changed between Second edition and Third Edition. Here the chapter numbers in the demo were changed to fit to Third edition. But all materials available in Second edition are still available in this version.

ACKNOWLEDGMENTS The project "Associate a level of error in predictions of models for agronomy" (CASDAR 2010-2013) and the French network "RMT modeling and agriculture", http://www.modelia.org) have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France.

Other contributions Juliette Adrian, Master2 internship (ACTA, http://www.modelia.org/moodle/mod/resource/view.php?id=1027), january-jully 2013.

Sylvain Toulet, Master2 internship (INRA, http://www.modelia.org/moodle/mod/resource/view.php?id=965), january-jully 2012.

Author(s)

Francois Brun (ACTA) francois.brun@acta.asso.fr, David Makowski (INRA), Daniel Wallach (INRA), James W. Jones (U.of Florida),

References

Working with Dynamic Crop Models (Elsevier), Third edition http://www.modelia.org

Calculate AIC, Akaike's Information Criterion

Description

This function calculate AIC criterion given a vector of observation, a vector of prediction and number of parameter. Note that number of parameters should include variance. AICcomplete is the same calculation of the AIC function of R (AICcomplete = n*log(RSS/n)+n+n*log(2*pi)+2*p, with p including variance). AICshort is the calculation described in chapter 6 Working with crop model (AICshort =n*log(RSS/n)+2*p, with p including variance). difference between AICcomplete and AICshort is AICcomplete-AICshort=n+n*log(2*pi) As you use AIC to compare models (with different number of parameters) on a same data (with same n, number of observation), you can use AICshort or AICcomplete.

Usage

AICf(Yobs, Ypred, npar)

Arguments

Value

a vector with AICcomplete and AICshort

Examples

x=c(1,2,3,4,5)
y=c(1.2,1.8,3.5,4.3,5.5)
fit = lm(y~x)
AIC(fit)
AICf(y,predict(fit),3) # 3 parameters : intercept, slope and variance

Bean gene-based models dataset

Description

Genetic data for the common bean (Phaseolus vulgaris L) that was based on a population created by C. E. Vallejos (personal communication; also see Bhakta et al., 2015, 2017) by crossing two widely-differing cultivars of bean (Calima, an Andean type, with Jamapa, a Mesoamerican type). Bean$marker : Bean Marker Data Bean$MET : Weather data on 5 Locations Bean$QTL : QTL Data Bean$modelpar : Dynamic Model parameters

Usage

Bean

Format

a List including 4 data.frame Bean$marker, Bean$MET, Bean$QTL, Bean$modelpar.

Source

C. E. Vallejos (personal communication) and Bhakta et al. (2015, 2017). Bhakta, M. S., V. A. Jones, C. E. Vallejos. 2015. Punctuated distribution of recombination hotspots and demarcation of pericentromeric regions in Phaseolus vulgaris L. PLoS ONE 10(1): https://doi.org/10.1371/journal.pone.0116822 Bhakta, M. S., S. A. Gezan, J. A. Clavijo Michelangeli, M. Carvalho, L. Zhang, J. W. Jones, K. J. Boote, M. J. Correll, J. Beaver, J. M. Osorno, R. Colbert, I. Rao, S. Beebe, A. Gonzalez, J. Ricaurte, and C. E.do Vallejos, 2017. A predictive model for time-to-flower in the common bean based on QTL and environmental variables. G3: Genes, Genomes, Genetics 7(12) 3901-3912. https://doi.org/10.1534/g3.117.300229.

Examples

# show the maker of JC1 to JC9 values for both parents (JAM and CAL)
# and 5 cRILS (RIJC001 to RIJC005)
Bean$marker[2:8,1:10]
# show the first value of weather data
head(Bean$MET)
# show the value of QTL
Bean$QTL[4:10,1:10]
# show the value of
Bean$modelpar

Phomopsis stem canker observations for Sunflower

Description

This dataset contains fraction intercepted photosynthetically active radiation (IPAR) and corresponding percent of girdling lesions at harvest (pclesions) for 43 fields. Phomopsis stem canker is a worldwide fungal disease of sunflower, which causes stem girdling lesions and a consequent reduction in yield. One wants to decide if the number of girdling lesions at harvest in the absence of early treatment will exceed 15 This data frame consist of a sample of fields with values for IPAR (ipar) and for the percent of girdling lesions at harvest (pclesions).

Usage

Sunflower_Phomopsis

Format

a RangedData instance, 1 row per observation.

Source

Debaeke, P., & Estragnat, A. (2009). Crop canopy indicators for the early prediction of Phomopsis stem canker (Diaporthe helianthi) in sunflower. Crop Protection, 28(9), 792-801. doi:10.1016/j.cropro.2009.04.011

National Wheat Yield evolution for Greece from FAO

Description

Wheat yield time series data in Greece from 1961 to 2010 yield.

Usage

WheatYieldGreece

Format

a RangedData instance, 1 row per measurement. Year, Yield : Wheat Yield (hectogram/hectare = 0.0001 ton/hectare)

Source

Food And Agricultural Organization of United Nations (FAO), FAOSTAT database, http://faostat.fao.org

Grain Protein Contents in Wheat Grains

Description

This dataset contains data for 43 plots. The column GPC corresponds to measured data, GPC.model1 and GPC.model2 correspond to the results obtained by two models. The other columns contain other information for each plot (technical practices) : number, tillage, max_water, preceding_crop, sow_date, Nendwinter, Nfertilizer, SPAD, NNI, Yield.

Usage

Wheat_GPC

Format

a RangedData instance, 1 row per plot.

Source

Barbottin et al. 2008

The CarbonSoil model - calculate daily values over designated time period

Description

Simple dynamic model of soil carbon content, with a time step of one year. The equations that describe the dynamics of this system are adapted from the Henin-Dupuis model described in Jones et al. (2004). The soil carbon content is represented by a single state variable: the mass of carbon per unit land area in the top 20 cm of soil in a given year (Z, kg.ha-1). It is assumed that soil C is known in some year, taken as the initial year. The yearly change in soil C is the difference between input from crop biomass and loss.

Usage

carbonsoil.model(R, b, U, Z1, duration)

Arguments

Value

Soil carbon years.

The CarbonSoil model - calculate change in soil carbon for one year

Description

Simple dynamic model of soil carbon content, with a time step of one year. The equations that describe the dynamics of this system are adapted from the Henin-Dupuis model described in Jones et al. (2004). The soil carbon content is represented by a single state variable: the mass of carbon per unit land area in the top 20 cm of soil in a given year (Z, kg.ha-1). It is assumed that soil C is known in some year, taken as the initial year. The yearly change in soil C is the difference between input from crop biomass and loss.

Usage

carbonsoil.update(Zy, R, b, Uy)

Arguments

Value

Soil carbon content for year+1.

The Carcass (growth of beef cattle) model with energy as input

Description

Model description. This model is proposed by Hoch et. al (2004) to represent the growth of cattle and the relative body composition of diferent types of animals depending on nuritionnal conditions. It simulates the dynamics of changes in the composition of the body fat and proteins according to nutrient intake. The system is represented by four state variables: the protein and fat in the carcass (resp. ProtC and LIPC) and other tissues (resp. ProtNC and LipNC) grouped under the name of the fifth district (again, gastrointestinal tract, skin .. .). These variables depend on time, the time step used is dt = 1 day. The model is defined by 20 equations, with a total of 18 parameters for the described process.

Usage

carcass.EMI.model(protcmax, protncmax, alphac, alphanc, gammac, gammanc,
  lip0, lipc1, lipnc1, beta, delta, amW, b0c, b1c, b0nc, b1nc, c0, c1,
  energie, PVi, duration)

Arguments

Value

matrix with ProtC,LipC,ProtNC,LipNC,PV

The Carcass model function for use with carcass.EMI.simule

Description

see carcass.EMI.model for model description.

Usage

carcass.EMI.model2(param, energie, PVi, duration)

Arguments

Value

data.frame with PV,ProtC,ProtNC,LipC,LipNC

Wrapper function to run Carcass model on several animals with different conditions

Description

wrapper function for multisimulation with carcass.EMI.model2

Usage

carcass.EMI.multi(param, list_individuals, energy, init_condition)

Arguments

Value

data.frame with id, ration ,duration, day, PV,ProtC,ProtNC,LipC,LipNC

Wrapper function to the Carcass model for multiple sets of parameter values

Description

Wrapper function to the Carcass model for multiple sets of parameter values

Usage

carcass.EMI.simule(X, energy, PVi, duration)

Arguments

Value

data.frame with PV,ProtC,ProtNC,LipC,LipNC

Define values of the parameters for the Carcass model

Description

Define parameters values

Usage

carcass.define.param(full = FALSE)

Arguments

Value

matrix with parameter values (nominal, binf, bsup). A data.frame if full=TRUE

Examples

carcass.define.param(full=TRUE)

The Carcass (growth of beef cattle) model

Description

Model description.

The model is defined by 20 equations, with a total of 19 parameters for the described process.

Usage

carcass.model(protcmax, protncmax, alphac, alphanc, gammac, gammanc, lip0,
  lipc1, lipnc1, beta, delta, k, b0c, b1c, b0nc, b1nc, c0, c1, cem,
  duration)

Arguments

Value

data.frame with ProtC,LipC,ProtNC,LipNC,PV

Data of growth of beef cattle for Carcass model

Description

This dataset is a list of 5 dataframe. list_individuals : identifiant for each indivual energy : Ration (type ration), Individu, time (week), energie (?) init_condition : Individu, Pvi (initial liveweight) observation_dynamic :Ration (3 levels "C","EM","F"), individu, time, PVobs observation_slaughter : Ration, individu, time, PVVobs, PVobs, ProtCobs, ProtNCobs, LipCobs, LipNCobs.

Usage

carcass_data

Format

a RangedData instance, 1 row per plot.

Source

Agabriel, J. (com.pers.)

Carrot weevil development model

Description

Model description. Simple model of developpement of carrot weevil.

Usage

carrot.weevil.model(tbase = 7, tteggs = 130, ttlarvae = 256,
  ttprepupae = 114, ttpupae = 130, ttadultpreovi = 91, weather,
  sdate = 1, ldate = 360)

Arguments

Value

data.frame with daily state variable

Data of growth of chicks

Description

This dataset content dynamic measurements of growth of chicks for different individuals and different strains. The data comes from a selection experiment on chicken initiated by F. Ricard Research Station on Poultry of INRA Nouzilly. The selection focuses on weight at 8 and 36 weeks and allowed to differentiate the following five strains:

strain 1 : X-+ (low at 8, but high at 36 weeks)

strain 2 : X+- (high at 8, but low at 36 weeks)

strain 3 : X++ (high weight at both ages)

strain 4 : X- (low weight for both ages)

strain 5 : X0 (control).

This is a sub-sample of 50 females in the last generation of selection with weight data (in g) at 12 different ages to complete measurement (0, 4, 6, 8, 12, 16, 20, 24; 28, 32, 36 and 40 weeks).

Usage

carcass_data

Format

a RangedData instance, 1 row : strain ; id_animal ; time (day) ; liveweight (g).

Source

Duval M., Robert-Granie C., Foulley J.-L. (2009) Estimation of heterogeneous variances in non linear mixed models via the SAEM algorithm with applications to growth curves in poultry. Journal de la Societe Francaise de Statistique, 150,65-83

Donnet S., Foulley J.-L., Samson A. (2010) Bayesian analysis of growth curves using mixed models defined by stochastic differential equations. Biometrics 66, 733-741

Jaffrezic F., Meza C., Foulley .J.-L., Lavielle M. (2006) The SAEM algorithm for the analysis of non linear traits in genetic studies. Genetics, Selection, Evolution,38, 583-

This dataset was used in a training session Biobayes (France, 2011) training session.

Albert I., Ancelet S., David O., Denis J.B., Makowski D., Parent E., Soubeyrand S. (2012) Methodes statistiques bayesiennes. Bases theoriques et applications en alimentation, environnement et genetique. FormaScience. Ecole-chercheurs INRA.

The Cotton model (dynamic for numbers of Cotton fruiting points).

Description

Model description. TO COMPLETE

Usage

cotton.model(TESQ, PMAX, AFL, AL, AOP, P1, P2, P3, P4, P5, PF, PSF, TSQ, P,
  PR, PT, tend)

Arguments

Value

data.frame with daily state variable

Define values of the parameters for the Epirice model

Description

Define parameters values

Usage

epirice.define.param()

Value

matrix with parameter values (nominal, binf, bsup)

The Epirice model (Disease model for rice)

Description

Model description. Adapted from Savary et al.(2012)

Usage

epirice.model(param, weather, sdate = 1, ldate = 120, H0 = 600)

Arguments

Value

data.frame with daily state variable

See Also

epirice.multi.simule

Wrapper function to run Epirice multiple times (for multiple sets of inputs)

Description

Wrapper function for epirice.model

Usage

epirice.multi.simule(param, multi.simule, all = FALSE)

Arguments

Value

matrix with AUDPC for each input vector

See Also

epirice.model

Read weather data for Epirice (southern Asia weather)

Description

Read weather data and format them for epirice.model

Usage

epirice.weather(working.year = NA, working.site = NA,
  weather = weather_SouthAsia)

Arguments

Value

data.frame with daily weather data for one or several site(s) and for one or several year(s)

Calcule multiple goodness-of-fit criteria

Description

This function is depreciated and will be remove from the package in future versions. Please use goodness.of.fit

Usage

evaluation.criteria(Ypred, Yobs, draw.plot = FALSE)

Arguments

Value

data.frame with the different evaluation criteria

Examples

# observed and simulated values
obs<-c(78,110,92,75,110,108,113,155,150)
sim<-c(126,126,126,105,105,105,147,147,147)
evaluation.criteria(sim,obs,draw.plot=TRUE)

The Exponential growth model of dynamic of population

Description

Exponential growth model of dynamic of population

Usage

exponential.model(a, Y0, duration = 40, dt = 1)

Arguments

Value

data.frame with Y for each time step

See Also

verhulst.update for the update function of the Verhulst model.

The Exponential growth model of dynamic of population - another form

Description

Exponential growth model of dynamic of population - another form

Usage

exponential.model.bis(a, Y0, duration = 40, dt = 1)

Arguments

Value

data.frame with Y for each time step

See Also

verhulst.update for the update function of the Verhulst model.

The Exponential growth model of dynamic of population - with improved Euler integration

Description

Exponential growth model of dynamic of population - with improved Euler integration

Usage

exponential.model.ie(a, Y0, duration = 40, dt = 1)

Arguments

Value

data.frame with Y for each time step

See Also

verhulst.update for the update function of the Verhulst model.

Calcule multiple goodness-of-fit criteria

Description

Calcule multiple goodness-of-fit criteria

Usage

goodness.of.fit(Yobs, Ypred, draw.plot = FALSE)

Arguments

Value

data.frame with the different evaluation criteria

Examples

# observed and simulated values
obs<-c(78,110,92,75,110,108,113,155,150)
sim<-c(126,126,126,105,105,105,147,147,147)
goodness.of.fit(obs,sim,draw.plot=TRUE)

Plot output of a Classical SEIR model for plant disease

Description

Plot the output of the Zadoks classical SEIR model for plant disease.

Usage

graph_epid(out, typel = "s", all = TRUE, param = TRUE)

Arguments

Value

plot

See Also

zakoks.original.model

Plot output of a Classical SEIR model for plant disease

Description

Plot the output of the Zadoks classical SEIR model for plant disease.

Usage

graph_epid_s(out, typel = "s", all = TRUE, param = TRUE)

Arguments

Value

plot

See Also

zakoks.original.model

The Lactation model

Description

Model description. This model is a model of lactating mammary glands of cattle described by Heather et al. (1983). This model was then inspired more complex models based on these principles. This model simulates the dynamics of the production of cow's milk. the system is represented by 6 state variables: change in hormone levels (H), the production and loss of milk secreting cells (CS), and removing the secretion of milk (M), the average quantity of milk contained in the animal (Mmean), the amount of milk removed (RM) and yield (Y). The model has a time step dt = 0.1 for regular consumption of milk by a calf. The model is defined by a few equations, with a total of fourteen parameters for the described process.

Usage

lactation.calf.model(cu, kdiv, kdl, kdh, km, ksl, kr, ks, ksm, mh, mm, p,
  mum, rc, duration, dt)

Arguments

Value

data.frame with CS, M, Mmoy, RM, day, week

Examples

lactation.calf.model2(lactation.define.param()["nominal",],300,0.1)

The Lactation model for use with lactation.calf.simule

Description

see lactation.calf.model for model description.

Usage

lactation.calf.model2(param, duration, dt)

Arguments

Value

data.frame with CS, M, Mmoy, RM, day, week

Examples

sim=lactation.calf.model2(lactation.define.param()["nominal",],6+2*7, 0.1)

Wrapper function to run the Lactation model for multiple sets of parameter values

Description

Wrapper function to run the Lactation model for multiple sets of parameter values

Usage

lactation.calf.simule(X, duration, dt)

Arguments

Value

data.frame with : number of paramter vector (line number from X), week, CS, M, Mmoy, RM, day, week

Define values of the parameters for the Lactation model

Description

values from Heather et al. (1983) for different scenarios

Usage

lactation.define.param(type = "calf")

Arguments

Value

matrix with parameter values (nominal, binf, bsup)

Examples

lactation.define.param()

The Lactation model with milking machine

Description

Model description. This model is a model of lactating mammary glands of cattle described by Heather et al. (1983). This model was then inspired more complex models based on these principles. This model simulates the dynamics of the production of cow's milk. the system is represented by 6 state variables: change in hormone levels (H), the production and loss of milk secreting cells (CS), and removing the secretion of milk (M), the average quantity of milk contained in the animal (Mmean), the amount of milk removed (RM) and yield (Y). The model has a time step dt = 0.001 for milking machines. The model is defined by a few equations, with a total of twenty parameters for the described process.

Usage

lactation.machine.model(cu, kdiv, kdl, kdh, km, ksl, kr, ks, ksm, mh, mm,
  p, mum, rma, t1, t2, t3, t4, t5, t6, duration, dt, CSi, Mi)

Arguments

Value

matrix with CS,M,Mmoy,RM

The Lactation model for use with lactation.machine.simule

Description

see lactation.calf.model for model description.

Usage

lactation.machine.model2(param, duration, dt, CSi, Mi)

Arguments

Value

data.frame with CS, M, Mmoy, RM, day, week

Define values of the parameters for the Magarey model

Description

Define values of the parameters for the Magarey model

Usage

magarey.define.param(species = "unkown")

Arguments

Value

matrix with parameter values (nominal, binf, bsup)

Examples

magarey.define.param(species="G.citricarpa")
magarey.define.param(species="Kaki.fungus")

The Magarey model

Description

Generic model of infection for foliar diseases caused by fungi (from Magarey et al.,2005).

Usage

magarey.model(T, Tmin, Topt, Tmax, Wmin, Wmax)

Arguments

Value

Wetness duration (W, hour). Either a scalar or a vector depending on T.

Examples

plot(1:35, magarey.model (1:35,7, 18, 30, 10, 42), type="l", xlab="T", ylab="W")

The Magarey model, taking a vector of parameters as argument

Description

Generic model of infection for foliar diseases caused by fungi (from Magarey et al.,2005).

Usage

magarey.model2(T, param)

Arguments

Value

W : Wetness duration (hour). Either a scalar or a vector depending on T.

Wrapper function to run the Magarey model multiple times (for multiple sets of inputs)

Description

Wrapper function to run the Magarey model multiple times (for multiple sets of inputs)

Example magarey.simule(magarey.define.param(),15)

Usage

magarey.simule(X, T, all = FALSE)

Arguments

Value

a table with wetness duration (W) for each parameter vector

Calculate effect of temperature on RUE for Maize

Description

Function to compute effect of temperature on RUE

Usage

maize.RUEtemp(T, RUE_max, T0, T1, T2, T3)

Arguments

Value

RUE value

maize biomass and leaf area data

Description

"observation data" for several site and year (site-year) in Europe. This data are fake observed date, derived from simulations with an error model.

Usage

maize.data_EuropeEU

Format

a RangedData instance, 1 row per measurement.

Source

NA

dataset of simulation for maize final biomass

Description

Simulation data for several site and year (site-year) in Europe. This data are from run of the original maize crop model with the R function maize.model for the 30 French sites and 17 years included in the dataset weather_FranceWest of the package ZeBook. Our training dataset includes 510 (30 sites * 17 years) simulated biomass values and the 510 corresponding series of input values. The input values are the three average temperatures T1, T2, T3 and the three average radiations RAD1, RAD2, RAD3 computed on 3 periods of the growing season. Period 1 : from day 1 to dat 50 (day of the year), period 2: from day 51 to day 100, period 3 : from day 101 to day 150.

Usage

maize.data_EuropeEU

Format

a RangedData instance, 1 row per simulation. Site, Year, T1, T2, T3, RAD1, RAD2, RAD3, B

Source

NA

See Also

maize.model, weather_FranceWest

Define values of the parameters for the Maize model

Description

Define parameters values

Usage

maize.define.param()

Value

matrix with parameter values (nominal, binf, bsup)

The basic Maize model.

Description

Model description. This model is a dynamic model of crop growth for Maize cultivated in potential conditions. The crop growth is represented by three state variables, leaf area per unit ground area (leaf area index, LAI), total biomass (B) and cumulative thermal time since plant emergence (TT). It is based on key concepts included in most crop models, at least for the "potential production" part. In fact, this model does not take into account any effects of soil water, nutrients, pests, or diseases,...

Usage

maize.model(Tbase, RUE, K, alpha, LAImax, TTM, TTL, weather, sdate, ldate)

Arguments

Details

The tree state variables are dynamic variables depending on days after emergence: TT(day), B(day), and LAI(day). The model has a time step dt of one day.
The model is defined by a few equations, with a total of seven parameters for the described process.
(1) TT(day+1) = TT(day)+dTT(day)
(2) B(day+1) = B(day)+dB(day)
(3) LAI(day+1) = LAI(day)+dLAI(day)
(4) dTT(day) = \max(\frac{TMIN(day)+TMAX(day)}{2}-Tbase;0)
(5) dB(day) = RUE*(1-e^{-K*LAI(day)*I(day)}),\ if\ TT(day)\le TTM
dB(day) = 0,\ if\ TT(day)>TTM
(6) dLAI(day) = alpha*dTT(day)*LAI(day)*\max(LAImax-LAI(day);0),\ if \ TT(day)\le TTL
dLAI(day) = 0,\ if\ TT(day)>TTL

Value

data.frame with daily TT, LAI,B

See Also

maize.model2, maize.define.param, maize.simule, maize.multisy, maize.simule240,maize.simule_multisy240

Examples

weather = maize.weather(working.year=2010, working.site=30,weather_all=weather_EuropeEU)
maize.model(Tbase=7, RUE=1.85, K=0.7, alpha=0.00243, LAImax=7, TTM=1200, TTL=700,
  weather, sdate=100, ldate=250)

The basic Maize model for use with maize.simule

Description

Wrapper pour maize.model

Usage

maize.model2(param, weather, sdate, ldate)

Arguments

Value

data.frame with daily TT, LAI,B

Examples

weather = maize.weather(working.year=2010, working.site=30,weather_all=weather_EuropeEU)
maize.model2(maize.define.param()["nominal",], weather, sdate=100, ldate=250)

Plot dynamic output of Muchow Maize model.

Description

Plot 6 graphs of main output variables of the Muchow Maize model.

Usage

maize.muchow.graph(res)

Arguments

See Also

mm.A.fct, mm.LN.fct, mm.FAS.fct, maize.multisy, mm.HI.fct,maize.muchow.model

Examples

# not run in package test
# res = maize.muchow.model(weather=maize.weather(working.year=2010, working.site=1))
# maize.muchow.graph(res)

Maize model, with harvest index and yield from Muchow et al. (1990)

Description

Maize model, with harvest index and yield. from Muchow RC, Sinclair TR, and Bennett JM (1990). Temperature and Solar Radiation Effects on Potential Maize Yield across Locations AGRONOMY JOURNAL, VOL. 82, MARCH-APRIL 1990

Usage

maize.muchow.model(Tbase1 = 8, TTE = 87, TTS = 67, Tbase2 = 0,
  TTRUE = 500, TTM = 1150, TLN = 20, AM = 596, RUE1 = 1.6,
  RUE2 = 1.2, K = 0.4, HImax = 0.5, Population = 7, sdate = 100,
  ldate = 365, weather)

Arguments

Value

data.frame with TT1, TT2, STADE, LN, LAI, B, HI, YIELD

See Also

mm.A.fct, mm.LN.fct, mm.FAS.fct, maize.multisy, mm.HI.fct,maize.muchow.graph

Examples

# not run in package test
# res = maize.muchow.model(weather=maize.weather(working.year=2010, working.site=1))
#res$FinalYield

Wrapping function to run maize model on several site-years

Description

Wrapping function to run maize model on several site-years

Usage

maize.multisy(param, list_site_year, sdate, ldate,
  weather_all = weather_EuropeEU)

Arguments

Value

a data.frame with simulation for all site-years, with the first column sy indicating the site-years

Wrapper function to run Maize model for multiple sets of input variables (site-year) and give Biomass at day240.

Description

Wrapper function to run Maize model for multiple sets of input variables (site-year) and give Biomass at day240.

Usage

maize.multisy240(param, liste_sy, sdate, ldate,
  weather_all = weather_EuropeEU)

Arguments

Value

mean biomass at day=240

Examples

maize.multisy240(maize.define.param()["nominal",],c("18-2006","64-2004") , sdate=100, ldate=250)

Wrapper function to run Maize model for multiple sets of parameter values

Description

wrapper for maize.model2

Usage

maize.simule(X, weather, sdate, ldate, all = FALSE)

Arguments

Value

matrix with maximum biomass for each parameter vector

Wrapper function to run Maize model multiple times for multiple sets of parameter values and give Biomass at day240

Description

Wrapper function for multiple simulation with Maize model

Usage

maize.simule240(X, weather, sdate, ldate, all = FALSE)

Arguments

Value

a matrix of biomass at day=240 for all combinations of parameters of X

Examples

sy="18-2006"
weather = maize.weather(working.year=strsplit(sy,"-")[[1]][2],
  working.site=strsplit(sy,"-")[[1]][1],weather_all=weather_EuropeEU)
maize.simule240(maize.define.param(),weather, sdate=100, ldate=250, all=FALSE)

Wrapper function to run Maize model for multiple sets of parameter values (virtual design) and multiple sets of input variables (site-year) and give Biomass at day240

Description

Wrapper function to run Maize model for multiple sets of input variables (site-year) and give Biomass at day240.

Usage

maize.simule_multisy240(X, liste_sy, sdate, ldate, all = FALSE)

Arguments

Value

a matrix of mean biomass at day=240 for all combinations of parameters of X

Examples

maize.simule_multisy240(maize.define.param(),c("18-2006","64-2004"),
  sdate=100, ldate=250, all=FALSE)

Read weather data for the Maize model

Description

Function to read weather data and format them for maize.model

Usage

maize.weather(working.year = NA, working.site = NA,
  weather_all = weather_FranceWest)

Arguments

Value

data.frame with daily weather data for one or several site(s) and for one or several year(s)

The Maize model with additional state variable CumInt

Description

Variant of the maize model

Usage

maize_cir.model(Tbase, RUE, K, alpha, LAImax, TTM, TTL, weather, sdate,
  ldate)

Arguments

Value

data.frame with daily TT, LAI,B

The Maize model with temperature dependent RUE and CumInt

Description

Variant of the maize.model

Usage

maize_cir_rue.model(Tbase, RUE_max, K, alpha, LAImax, TTM, TTL, weather,
  sdate, ldate)

Arguments

Value

data.frame with daily TT, LAI,B

The Maize model with temperature dependent RUE, CumInt and ear growth

Description

Variant of the maize.model

Usage

maize_cir_rue_ear.model(Tbase, RUE_max, K, alpha, LAImax, TTM, TTL,
  weather, sdate, ldate)

Arguments

Value

data.frame with daily TT, LAI,B

Expanded leaf area function for Muchow et al. (1990) Maize model

Description

Compute fully expanded area by leaf number (A, cm2)

Usage

mm.A.fct(LN, AM, LNM, a1 = -0.0344, a2 = 0.000731)

Arguments

Value

vector of Expanded leaf area

See Also

maize.muchow.model, mm.LN.fct, mm.FAS.fct, maize.multisy, mm.HI.fct,maize.muchow.graph

Examples

barplot(mm.A.fct(LN=1:20, AM=750, LNM=12),names.arg=1:20,
horiz=TRUE,xlab="leaf area (cm2)",ylab="leaf number")

Senescence function for Muchow et al. (1990) Maize model

Description

Senesced fraction of total leaf area (FAS) increase with thermal units (TU) from emergence

Usage

mm.FAS.fct(TT, TTE, c1 = 0.00161, c2 = 0.00328)

Arguments

Value

Senesced fraction of total leaf area

See Also

maize.muchow.model, mm.A.fct, mm.LN.fct, maize.multisy, mm.HI.fct,maize.muchow.graph

Examples

plot(1:2500,mm.FAS.fct(1:2500,TTE=87))

Harvest index function for Muchow et al. (1990) Maize model

Description

Compute the harvest index.

Usage

mm.HI.fct(day, daysilking, HImax, d1 = 0.015, d2 = 3)

Arguments

Value

Harvest index

See Also

maize.muchow.model, mm.A.fct, mm.LN.fct, maize.multisy, mm.FAS.fct,maize.muchow.graph

Examples

plot(1:350, mm.HI.fct(1:350, 200, 0.75), type="l")

Leaf number function for Muchow et al. (1990) Maize model

Description

Leaf number as a function of thermal time

Usage

mm.LN.fct(TT1, TTE, b1 = 2.5, b2 = 0.00225, TLN = 20)

Arguments

Value

Leaf number

See Also

maize.muchow.model, mm.A.fct, mm.FAS.fct, maize.multisy, mm.HI.fct,maize.muchow.graph

Examples

plot(1:1000,mm.LN.fct(1:1000,TTE=87))

Generate a random plan as a data frame. Columns are parameters. Values have triangle distribution

Description

according to nominal, minimal and maximal values defined in a model.factors matrix

Usage

param.rtriangle(model.factors, N)

Arguments

Value

parameter matrix of dim = (N, p)

Generate a random plan as a data frame. Columns are parameters. Values have uniform distribution

Description

according to minimal and maximal values defined in a model.factors matrix

Usage

param.runif(model.factors, N)

Arguments

Value

parameter matrix of dim = (N, p)

The PopulationAge model (Population Dynamics with Age Classes) - matrix form

Description

Population Dynamics Model with Age Classes for an insect Exactly the same model as population.age.model, but written as a matrix computation. It's possible for this model. It's really more efficient and reduce computer time by 6! 7 states variables E : egg stage. homogenous population (density) (number per ha) L1 : larvae1 stage. homogenous population (density) (number per ha) L2 : larvae2 stage. homogenous population (density) (number per ha) L3 : larvae3 stage. homogenous population (density) (number per ha) L4 : larvae4 stage. homogenous population (density) (number per ha) P : pupae stage. homogenous population (density) (number per ha) A : adult stage. homogenous population (density) (number per ha)

Usage

population.age.matrix.model(rb = 3.5, mE = 0.017, rE = 0.172,
  m1 = 0.06, r12 = 0.217, m2 = 0.032, r23 = 0.313, m3 = 0.022,
  r34 = 0.222, m4 = 0.02, r4P = 0.135, mP = 0.02, rPA = 0.099,
  mA = 0.027, iA = 0, duration = 100, dt = 1)

Arguments

Value

data.frame with values for state variables for each time step.

The PopulationAge model (Population Dynamics with Age Classes)

Description

Population Dynamics Model with Age Classes for an insect

Usage

population.age.model(rb = 3.5, mE = 0.017, rE = 0.172, m1 = 0.06,
  r12 = 0.217, m2 = 0.032, r23 = 0.313, m3 = 0.022, r34 = 0.222,
  m4 = 0.02, r4P = 0.135, mP = 0.02, rPA = 0.099, mA = 0.027,
  iA = 0, duration = 100, dt = 1)

Arguments

Value

data.frame with values for state variables for each time step.

The PopulationAge model (Population Dynamics with Age Classes) - ode form

Description

Population Dynamics Model with Age Classes for an insect Exactly the same model as population.age.model, but written as an ordinary differential equation system (ode) with deSolve package. 7 states variables E : egg stage. homogenous population (density) (number per ha) L1 : larvae1 stage. homogenous population (density) (number per ha) L2 : larvae2 stage. homogenous population (density) (number per ha) L3 : larvae3 stage. homogenous population (density) (number per ha) L4 : larvae4 stage. homogenous population (density) (number per ha) P : pupae stage. homogenous population (density) (number per ha) A : adult stage. homogenous population (density) (number per ha)

Usage

population.age.model.ode(rb = 3.5, mE = 0.017, rE = 0.172,
  m1 = 0.06, r12 = 0.217, m2 = 0.032, r23 = 0.313, m3 = 0.022,
  r34 = 0.222, m4 = 0.02, r4P = 0.135, mP = 0.02, rPA = 0.099,
  mA = 0.027, iA = 0, duration = 100, dt = 1, method = "euler")

Arguments

Value

data.frame with values for state variables for each time step.

The PredatorPrey model (Predator-Prey Lotka-Volterra with logistic equation for prey)

Description

Predator-Prey Lotka-Volterra model (with logistic prey)

Usage

predator.prey.model(grH = 1, kH = 10, mrH = 0.2, eff = 0.5,
  mrA = 0.2, H0 = 1, A0 = 2, duration = 200, dt = 1,
  method = "euler")

Arguments

Value

data.frame with daily H and A

Build the q.arg argument for the FAST function (sensitivity analysis)

Description

according to minimal and maximal values defined in a model.factors matrix

Usage

q.arg.fast.runif(model.factors)

Arguments

Value

a list of list

Wheat grain weight measurements after anthesis

Description

Darroch and Baker (1990) studied grain filling in three spring wheat genotypes. The data are seed weights of the spring wheat cultivar Neepawa in three different years 1986, 1987, 1988. These data were numerised from figure of the article, so they present slight difference with original data.

Usage

seedweight.data

Format

a RangedData instance, 1 row per measurement. DD = Degree Days after anthesis; seedweight = Wheat grain weight (mg)

Source

Darroch, B A, and R J Baker. 1990. "Grain filling in three spring wheat genotypes: statistical analysis." Crop Science 30(3):525-529.

The SeedWeight model

Description

The SeedWeight model is a logistic model of grain weight over time in wheat. The model was proposed by Darroch & Baker (1990) in a study of grain filling in three spring wheat genotypes. This model has a single input variable, degree days after anthesis noted DD, and three parameters, noted W, B and C. Parameters are estimated from observations.

Usage

seedweight.model(DD, W, B, C)

Arguments

Value

Seed Weight for each TT

Examples

plot(1:500,seedweight.model(1:500, W=30,B=4,C=0.020),type="l",
   xlab="degree days after anthesis", ylab="grain weight")

Computation of threshold.measures

Description

Computation of threshold.measures

Usage

threshold.measures(Yobs, Ypred, p, d, units = "")

Arguments

Value

data.frame with the different evaluation criteria

Examples

# observed and simulated values
obs<-c(78,110,92,75,110,108,113,155,150)
sim<-c(126,126,126,105,105,105,147,147,147)
threshold.measures(obs,sim,80,1.0)

The Verhulst (logistic) model - calculate daily values over designated time period

Description

The Verhulst (logistic) model - calculate daily values over designated time period

Usage

verhulst.model(a, k, Y0, duration)

Arguments

Value

data.frame with daily Y

See Also

verhulst.update for the update function of the Verhulst model.

Examples

plot(verhulst.model(0.08,100,1,100), type="l", ylim=c(0,115),
   xlab="day", ylab="Y, population density",lwd=2)

The Verhulst (logistic) model - calculate change for one day

Description

The Verhulst (logistic) model - calculate change for one day

Usage

verhulst.update(Y, a, k)

Arguments

Value

state variable at Y(t=day+1)

See Also

verhulst.model for the integration loop function of the Verhulst model.

Define values of the parameters for the WaterBalance model

Description

Define values of the parameters for the WaterBalance model

Usage

watbal.define.param()

Value

matrix with parameter values (nominal, binf, bsup)

WaterBalance model - calculate soil water over designated time period

Description

WaterBalance model - calculate soil water over designated time period

Usage

watbal.model(param, weather, WP, FC, WAT0 = NA)

Arguments

Value

data.frame with daily RAIN, ETR, Water at the beginning of the day (absolute : WAT, mm and relative value : WATp, -)

WaterBalance model - Variant with another order of calculation and ARID index

Description

WaterBalance model - Variant with another order of calculation and ARID index

Usage

watbal.model.arid(WHC, MUF, DC, z, CN, weather, WP, FC, WAT0 = NA)

Arguments

Value

data.frame with daily RAIN, ETR, Water at the beginning of the day (absolute : WAT, mm and relative value : WATp, -)

Soil water content measurements and associated simulations with WaterBalance model

Description

Data of soil water content from Luc Champolivier (CETIOM), in En Crambade (31, France), on canola without irrigation in 2008. sonde Diviner 2000 (from Sentek Pty Ltd) Simulation are from watbal.model, with an initial water content estimated from measurement with Diviner 2000.

Usage

watbal.simobsdata

Format

a RangedData instance, 1 row per day : Weather : day / RAIN / ETr / simulation : WAT / WATp / ARID observation :t1_WATp_0_40cm / t2_WATp_0_40cm / t3_WATp_0_40cm / WATp_SF.mean / WATp_SF.var

Source

CETIOM, Luc Champolivier (2008).

WaterBalance model - calculate change in soil water for one day

Description

WaterBalance model - calculate change in soil water for one day

Usage

watbal.update(WAT0, RAIN, ETr, param, WP, FC)

Arguments

Value

WAT1 : Water at the beginning of the day+1 (mm).

Read weather data for the WaterBalance model (West of France Weather)

Description

Read weather data for the WaterBalance model (West of France Weather)

Usage

watbal.weather(working.year = NA, working.site = NA)

Arguments

Value

data.frame with daily weather data for one or several site(s) and for one or several year(s)

Weather serie for Europe EU from NASA POWER agroclimatology

Description

This contemporary daily climate dataset for Europe covers the period 1st January 2001 to 31 December 2010 with 10 complete years of data. It cover a part of Europe) with an elevation less than 500m. The dataset was was extrated from the NASA Langley Research Center POWER Project which provide agroclimatology dataset (Chandler et al., 2004). It was funded through the NASA Earth Science Directorate Applied Science Program This climate datasetcontains daily estimates of precipitation, mean, minimum and maximum temperature, relative humidity, dew point, solar radiation and wind speed with global coverage at one degree resolution (approximately 111 km at the equator). The NASA POWER agroclimatology data are derived from various sources: solar radiation from satellite observations, meteorological data from the Goddard Earth Observing System global assimilation model version 4 (GEOS-4), and precipitation from the Global Precipitation Climate Project and Topical Rainfall Measurement Mission. A full description can be found at https://power.larc.nasa.gov/common/php/POWER_AboutAgroclimatology.php Elevation (Altitude) were retrive from Aster Global Digital Elevation Model by using the Webservice api.geonames.org/astergdem? Sample are: ca 30m x 30m, between 83N and 65S latitude. Result : a single number giving the elevation in meters according to aster gdem, ocean areas have been masked as "no data" and have been assigned a value of -9999 Example http://api.geonames.org/astergdem?lat=50.01&lng=10.2&username=demo

Usage

weather_EuropeEU

Format

a RangedData instance, 1 row per day. SRAD daily Insolation Incident On A Horizontal Surface (MJ/m^2/day) T2M Average Air Temperature At 2 m Above The Surface Of The Earth (degrees C) TMIN Minimum Air Temperature At 2 m Above The Surface Of The Earth (degrees C) TMAX Maximum Air Temperature At 2 m Above The Surface Of The Earth (degrees C) RH2M Relative Humidity At 2 m ( TDEW Dew/Frost Point Temperature At 2 m (degrees C) RAIN Average Precipitation (mm/day) WIND Wind Speed At 10 m Above The Surface Of The Earth (m/s)

Source

http://power.larc.nasa.gov/ and http://asterweb.jpl.nasa.gov/gdem.asp and http://www.geonames.org/about.html

Weather series for western France from NASA POWER agroclimatology

Description

This contemporary daily climate dataset for West of France covers the period 1st January 1984 to 31 December 2011. The precipitation data is limited to the period Jan-1997 Aug-2009, thus only 12 complete years of data were available for analysis involving precipitation(1997 to 2009). It cover main part of West part of France defined as a rectangle. The dataset was extracted from the NASA Langley Research Center POWER Project which provide agroclimatology dataset (Chandler et al., 2004). It was funded through the NASA Earth Science Directorate Applied Science Program This climate dataset contains daily estimates of precipitation, mean, minimum and maximum temperature, relative humidity, dew point, solar radiation and wind speed with global coverage at one degree resolution (approximately 111 km at the equator). The NASA POWER agroclimatology data are derived from various sources: solar radiation from satellite observations, meteorological data from the Goddard Earth Observing System global assimilation model version 4 (GEOS-4), and precipitation from the Global Precipitation Climate Project and Topical Rainfall Measurement Mission. A full description can be found at https://power.larc.nasa.gov/common/php/POWER_AboutAgroclimatology.php

Usage

weather_FranceWest

Format

a RangedData instance, 1 row per day.

Source

http://power.larc.nasa.gov/

Weather series for Gainesville (FL, USA) years 1982 and 1983

Description

This daily climate dataset for Gainesville (FL, USA) years 1982 and 1983 covers the period 1st January 1982 to 31 December 1983 with 2 complete years of data with precipitation. The dataset was provide by JJW Jones and the university of Florida to run simulation of the publication of Muchow et al. (1990). This climate dataset contains daily estimates of precipitation (RAIN), minimum (Tmin) and maximum temperature (Tmax), solar radiation (I), photosynthically active radiation (PAR)

Usage

weather_GNS

Format

a RangedData instance, 1 row per day.

Source

University of Florida

Weather series for southern Asia from NASA POWER agroclimatology

Description

This contemporary daily climate dataset for South Asia covers the period 1st January 1997 to 31 December 2008 with 12 complete years of data with precipitation. It cover a part of South Asia (North-East of India, Bangladesh, Myanmar, Neapal) with an elevation less than 2500m. The dataset was extracted from the NASA Langley Research Center POWER Project which provide agroclimatology dataset (Chandler et al., 2004). It was funded through the NASA Earth Science Directorate Applied Science Program This climate dataset contains daily estimates of precipitation, mean, minimum and maximum temperature, relative humidity, dew point, solar radiation and wind speed with global coverage at one degree resolution (approximately 111 km at the equator). The NASA POWER agroclimatology data are derived from various sources: solar radiation from satellite observations, meteorological data from the Goddard Earth Observing System global assimilation model version 4 (GEOS-4), and precipitation from the Global Precipitation Climate Project and Topical Rainfall Measurement Mission. A full description can be found at https://power.larc.nasa.gov/common/php/POWER_AboutAgroclimatology.php Elevation (Altitude) were retrive from Aster Global Digital Elevation Model by using the Webservice api.geonames.org/astergdem? Sample are: ca 30m x 30m, between 83N and 65S latitude. Result : a single number giving the elevation in meters according to aster gdem, ocean areas have been masked as "no data" and have been assigned a value of -9999 Example http://api.geonames.org/astergdem?lat=50.01&lng=10.2&username=demo

Usage

weather_SouthAsia

Format

a RangedData instance, 1 row per day.

Source

http://power.larc.nasa.gov/ and http://asterweb.jpl.nasa.gov/gdem.asp and http://www.geonames.org/about.html

Define parameter values of the Weed model

Description

Define parameter values of the Weed model

Usage

weed.define.param()

Value

matrix with parameter values (nominal, binf, bsup)

The Weed model - calculate daily values over designated time period

Description

The Weed model - calculate daily values over designated time period

Usage

weed.model(param, weed.deci)

Arguments

Value

data.frame with annual values of yield

Wrapper function to run the Weed model multiple times (for multiple sets of inputs)

Description

Wrapper function to run the Weed model multiple times (for multiple sets of inputs)

Usage

weed.simule(X, weed.deci)

Arguments

Value

matrix with Yield for year 3 for each parameter vector

The Weed model - calculate change for one year

Description

The Weed model - calculate change for one year

Usage

weed.update(d, S, SSBa, DSBa, Soil, Crop, Herb, param)

Arguments

Value

a vector with values of state variables for year+1

Classical SEIR model for plant diseases from Zadoks (1971)

Description

Model description. This model is a classical SEIR model for plant disease. It was written from it description included in the original publication of Zadoks (1971)

Usage

zakoks.original.model(nlpd = 4 * 10, nipd = 1 * 10, dmfr = 16,
  SITE0 = 5 * 10^9, weather, sdate = 145, ldate = 145 + 50,
  XLAT0 = 1)

Arguments

Details

This model is a classical SEIR model proposed by Zadoks (1971) to simulate epidemics of diseases of crops. It is a Susceptible-Exposed-Infectious-Removed (SEIR) model. This simple model of an epidemic is based on the epidemiological concepts "latent period", "infectious period", and "multiplication factor". The crop is considered to consist of a large but finite number of infectious sites. The physical dimensions of an infectious site roughly coincide with the reproductive unit of the parasite studied. Different pathosystems (with different infectious site definitions) can be considered with this model. A full description, is available in the original paper: The model has four essential state variables representing the number of sites in each state XVAC for vacant (healthy) sites, XLAT for latent site, XINF for infectant sites and XCTR for the cumulative total of removal (post infectious) sites. Two supplementary variables based on the state variables are used defined as XTO1 = XLAT+XINF+XCTR and XSEV = XINF+XCTR. Fluxes or rates between the state variables are defined as rocc for occupation, rapp for apparition and rrem for removal. The model has a time step of one day (dt=1). The system modeled is one hectare of a wheat crop.

Value

list with a data.frame with daily day, DACE, XVAC, XLAT, XINF, XCTR ,XTO1, XSEV= XSEV, severity and a vector of parameter value (nlpd, nipd, dmfr, SITE0).

Source

Script written from equation described in Zadoks, J.C. 1971. Systems Analysis and the Dynamics of Epidemics. Phytopathology. 61:441-598.

See Also

epirice.model

Examples

weather=subset(weather_FranceWest, WEYR==1997 & idsite==39)
out=zakoks.original.model(nlpd=4*10,nipd=1*10,dmfr=16,SITE0 = 5*10^9,
weather, sdate = 145, ldate = 145+50 , XLAT0=1)
plot(out$sim$DACE,out$sim$severity, type="l")