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ANNEX SECTION

ANNEX 1

PROGRAMME

Day Sessions Monday

9:00-9:30 Welcome, introduction to training sessions, program description. Maurits van den Berg

9:30-10:00 Overview Cuban agriculture, its challenges, and current research activities to address them. Expectations regarding BASAL and the applicability of BioMA in particular. Ransés Vazquez

10:00-10:40 BioMA modeling framework: purpose, functioning, strengths and limitations. Overview of models currently implemented within the BioMA framework. Fabien Ramos

10:40-11:00 Coffee break

11:00-12:00 Conducting BioMA runs: Introduction Davide Fumagalli

12:00-2:00 Lunch (JRC Mensa) and break

2:00-3:30 Principles of Agricultural Modeling Maurits van den Berg

3:30-4:00 Tea break

4:00-5:30 Conducting BioMA runs: practical exercise, questions and discussion. Davide Fumagalli

Tuesday 9:00-9:30 Discussion on hardware and software architecture- general requirements

Antonio Zucchini

9:30-10:30 Programmers: Discussion of details on

hardware and software architecture

Antonio Zucchini

Modelers: WOFOST and CROPSYST model overview and applications Maurits van den Berg


11:00-12:00 Programmers: Discussion of details on

hardware and software architecture

Antonio Zucchini

Modelers: STICS model overview and applications for pasture systems Rémi Lecerf


2:00-3:30 WARM model overview, applications, practical exercise and demonstration. Simone Bregaglio and Giovanni Cappelli


4:00-5:30 WARM model continuation Simone Bregaglio and Giovanni Cappelli

Wednesday

9:00-10:30 Develop BioMA modeling solutions - Introduction to Bioma model layer - Step by step training for the creation of a model layer modeling solution (exercise 1) Davide Fumagalli


11:00-11:50 Exercise 1 continuation


1:40-2:50 Optimizer component, Davide Fanchini

2:50-3:30 Discussion session: Data requirements, availability and procurement strategies - Weather - Soils - Crop parameters - Agro-management All BASAL team, moderated by Joysee Rodriguez

3:30-4:00 Tea/coffee break

4:00-5:30 Discussion session on data requirements, continuation

Thursday 9:00-10:30 Develop BioMA modeling solutions

- Independent work for the creation of a model layer modeling solution (exercise 2) Davide Fumagalli


11:00-11:50 Exercise 2 continuation


1:40-3:30 Develop BioMA modeling solutions - Introduction to Bioma composition layer - Step by step training for the creation of a composition layer modeling solution (exercise 3) - Independent work for the creation of a composition layer modeling solution (exercise 4) Davide Fumagalli

3:30-4:00 Coffee break 4:30-5:20 Exercise 4 continuation

Friday 9:00-10:30 Develop BioMA modeling solutions

- Introduction to exercise 5: Introduction to Bioma configuration layer - Step by step guide for the creation of a configuration layer modeling solution (exercise 5) Davide Fumagalli


11:00-12:00 Continuation exercise 5

12:00-1:30 Lunch (JRC Mensa)

1:30-2:20 Preparation of presentations on exercises participants

2:20-3:30 Presentations from participants

3:31-4:00 Tea/coffee break

4:01-5:30 - Any remaining matters - Final discussion; Expectations revisited Evaluation of training course

ANNEX 2

BioMA workshop

Participant's feedback

The four Cuban participants were asked to fill out the form below, anonymously, immediately after

the closure of the training, simply by ticking a field for each item. In this compilation, we indicated

how many participants ticked each field and added the average score for each item and category

in the last column.

Event: Basal - BioMA training workshopJRC

Date(s): 2-6 September 2013

Location: Ispra

Organiser: JRC, MARS unit

SCALE 1 2 3 Average score

Event's preparation Below expectations

Met expectations

Above expectations

2.0

Programme 4 2.0

Objectives 1 3 1.75

Selection of speakers 3 1 2.25

Event's delivery Below

expectations

Met

expectations

Above

expectations

2.44

Contents, quality of presentations 3 1 2.25

Discussion time / interaction

between participants 2 2 2.37

Workshops / sub-sessions 3 1 2.25

Balance between sessions 3 1 2.25

Speakers performance 2 3 2.75

Supporting material 3 2.25

Provision of additional resources

(useful links, downloads, contacts) 1 3 2.75

SCALE 1 2 3 Average

Organisation and Logistics Below expectations

Met expectations

Above expectations

3.0

Organisation, communication with

the participants 4 3.0

Meeting venue 4 3.0

Transport from airport to hotel 4 3.0

Hotel 2 2 2.5

Transport from hotel to venue 4 3.0

Lunches 4 3.25

Welcome dinner 4 3.25

Content Below expectations

Met expectations

Above expectations

2.56

Capacity of the training to meet

your learning objectives and its relevance for your work

2 2 2.12

Quality and accuracy of contents 3 3.0

Methodology Below

expectations

Met

expectations

Above

expectations

2.43

Length of the course and balance

between theory and practice 1 3 1.87

Possibility of interaction with trainer

and other participants 4 3.0

Learning Resources (Manuals,

Presentation Material, Hand-outs, etc)

Below

expectations

Met

expectations

Above

expectations

2.62

Usefulness and usability of course

material/presentations 2 2 2.5

Provision of additional resources (useful links, downloads, contacts)

1 3 2.75

Trainer / Facilitator Below expectations

Met expectations

Above expectations

2.87

Trainer's communication and interaction

3 1 2.75

Trainer's knowledge of the topic 4 3.0

General Comments Below expectations

Met expectations

Above expectations

Overall evaluation of the event 1 3 2.75

Any additional comment (especially for explaining the reasons for “below expectations")

Direct quotes from participants written comments:

The BioMA platform is fantastic in design, very tunable, but this implies that the time was not enough to comprehend and assimilate everything. We had no time to write down the full experience speakers

transmitted us at every step. [the software] is very complicated in itself and we needed more time to repeat each practice. We also need a procedure specific guide of each of the processes reviewed. Orally expressed comments:

I liked the training, and now we understand better BioMA and how to use the tools for modifying it, but we are not agronomist, so we need others to give us the procedures, or

components that will be added. For initiate learning in modifying

it we need more exercises like the ones studied here to practice adding new strategies or components. With our programming skills we can easily code any strategies but we need some more exercises to fully learn the steps in BioMA framework. We could do it independently if we have a good guide, and additional exercises.

There are too many steps involved in setting each model run, and I am not sure I will be able to remember all the necessary steps. We need a more specified guide that provides step by step each of the process either for running a ready model solution or for developing a new one. It could be even better if

the guides are developed with the ready initial tool for Cuba and maybe even in Spanish. More training will be needed and it could be great if the Davide Fumagalli could also be in the training in Cuba since his years of

experience with allow for fast clarification on technical problems and issues that often come when experimenting with modifying

previous versions. Maybe we needed more time for really assimilating the tool to a level that will be necessary to train others in our Institute.

ANNEX 3

List of Participants and Contributors

Name Contact Information

Instituto de Metereologia-Cuba INSMET

Roger Rolando Rivero Jaspe [email protected]

Ransés José Vázquez Montenegro Ransé[email protected] Ransé[email protected]

Malena Silva Lorenzo [email protected]

Frank Ernesto Pérez Acuña [email protected]

Joint Research Center

Maurits Van den Berg [email protected]

Stefan Niemeyer [email protected]

Davide Fumagalli [email protected]

Antonio Zucchini [email protected]

Fabien Ramos [email protected]

Davide Fanchini [email protected]

Rémi Lecerf [email protected]

Joysee Rodriguez Baide [email protected]

Beatriz Vidal-Legaz [email protected]

Ezio Crestaz [email protected]

University of Milano

Simone Ugo Bregaglio [email protected]

Giovanni Cappelli [email protected]

mailto:[email protected]

















ANNEX 4

List of materials in electronic format

BioMA Documentation

JRC-MARS. 2013. BioMA Framework User Guide. Release: 1. Issue: 3.

JRC-MARS. 2013. BioMA Composition Layer. Reference Documentation, Release: 1, Issue: 1.

JRC-MARS. 2013. BioMA Spatial User Guide. Release: 1, Issue: 3.

JRC-MARS. 2013. CropSyust Modeling Solutions: Reference Documentation. Release: 1. Issue: 1.

JRC-MARS. 2013. GDD, Graphic Data Display User Guide. Release: 1, Issue: 2.

JRC-MARS. 2013. CLIC, Composition Layer Interactive Code: User Guide. Release: 1, Issue: 2.

JRC-MARS. 2013. DCC, Domain Classes Coder: User Guide. Release: 1, Issue: 2.

JRC-MARS. 2013. MDV, Map Data Visualizer: User Guide. Release: -, Issue: 2.

JRC-MARS. 2013. MCE, Model Component Explorer: User Guide. Release: 1, Issue: 2.

JRC-MARS. 2013. MPE, Model Parameter Editor: User Guide. Release: 1, Issue: 2.

JRC-MARS. 2013. SCC, Strategy Class Coder: User Guide. Release: 1, Issue: 2.

Davide Fumagalli.2013. Handout for BioMA training: BASAL

Software

BioMA and added components: Bioma Spatial, CLIC, DCC, MCE, MPE, SCC.

Auxiliary software: SharpDevelop 4.3, SSCERuntime-ENU, SQL CE toolbox

Other files and folders prepared to use with training handout: CropSyst, WARM, ClimIndices, and ClimPest

modeling solutions, codes to be used for exercise on Model Layer (1 and 2) and final result files as

reference, exercise on Composition Layer (3 and 4) and final results files for reference, and auxiliary

weather provider to develop the exercise.

Other Background Literature:

Beaudoin, N., Launay, M., Sauboua, E., Ponsardin, G., & Mary, B. (2008). Evaluation of the soil crop model

STICS over 8 years against the “on farm” database of Bruyères catchment. European Journal of Agronomy,

29(1), 46–57. doi:10.1016/j.eja.2008.03.001

Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., & van Ittersum, M. (2013). A regional implementation of

WOFOST for calculating yield gaps of autumn-sown wheat across the European Union. Field Crops Research,

143, 130–142. doi:10.1016/j.fcr.2012.11.005

Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Burger, P. (2003). An overview of the crop

model STICS. European Journal of agronomy, 18(3), 309–332.

Brisson, Nadine, Gate, P., Gouache, D., Charmet, G., Oury, F.-X., & Huard, F. (2010). Why are wheat yields

stagnating in Europe? A comprehensive data analysis for France. Field Crops Research, 119(1), 201–212.

doi:10.1016/j.fcr.2010.07.012

Caubel, J., Launay, M., Lannou, C., & Brisson, N. (2012). Generic response functions to simulate climate-

based processes in models for the development of airborne fungal crop pathogens. Ecological Modelling,

242, 92–104. doi:10.1016/j.ecolmodel.2012.05.012

Confalonieri, R., Bregaglio, S., Donatelli, M., Tubiello, F., & Fernandes, E. (2012). Agroecological Zones

Simulator (AZS): A component based, open-access, transparent platform for climate change–crop

productivity impact assessment in Latin America. Retrieved from

http://www.iemss.org/iemss2012/proceedings/C3_0915_Confalonieri_et_al.pdf

Confalonieri, R., Donatelli, M., Bregaglio, S., Stella, T., Negrini, G., & Donatelli, M. (2012a). An extensible,

multi-model software library for simulating crop growth and development. In International Environmental

Modelling and Software Society (iEMSs) 6th International Congress, Leipzig, Germany. Retrieved from

http://www.iemss.org/sites/iemss2012/proceedings/C3_0836_Confalonieri_et_al.pdf

Confalonieri, R., Donatelli, M., Bregaglio, S., Stella, T., Negrini, G., & Donatelli, M. (2012b). An extensible,

multi-model software library for simulating crop growth and development. In International Environmental

Modelling and Software Society (iEMSs) 6th International Congress, Leipzig, Germany. Retrieved from

http://www.iemss.org/sites/iemss2012/proceedings/C3_0836_Confalonieri_et_al.pdf

Confalonieri, Roberto, Acutis, M., Bellocchi, G., & Donatelli, M. (2009). Multi-metric evaluation of the

models WARM, CropSyst, and WOFOST for rice. Ecological Modelling, 220(11), 1395–1410.

doi:10.1016/j.ecolmodel.2009.02.017

Confalonieri, Roberto, Bellocchi, G., Tarantola, S., Acutis, M., Donatelli, M., & Genovese, G. (2010).

Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations, climates

and methods of analysis on model sensitivity to crop parameters. Environmental Modelling & Software,

25(4), 479–488. doi:10.1016/j.envsoft.2009.10.005

Confalonieri, Roberto, Bregaglio, S., Rosenmund, A. S., Acutis, M., & Savin, I. (2011). A model for simulating

the height of rice plants. European Journal of Agronomy, 34(1), 20–25. doi:10.1016/j.eja.2010.09.003

Confalonieri, Roberto, Rosenmund, A. S., & Baruth, B. (2009). An improved model to simulate rice yield.

Agronomy for Sustainable Development, 29(3), 463–474. doi:10.1051/agro/2009005

Constantin, J., Beaudoin, N., Launay, M., Duval, J., & Mary, B. (2012). Long-term nitrogen dynamics in

various catch crop scenarios: Test and simulations with STICS model in a temperate climate. Agriculture,

Ecosystems & Environment, 147, 36–46. doi:10.1016/j.agee.2011.06.006

Corre-Hellou, G., Faure, M., Launay, M., Brisson, N., & Crozat, Y. (2009). Adaptation of the STICS intercrop

model to simulate crop growth and N accumulation in pea–barley intercrops. Field Crops Research, 113(1),

72–81. doi:10.1016/j.fcr.2009.04.007

Díaz-Ambrona, C. G. H., O’Leary, G. J., Sadras, V. O., O’Connell, M. G., & Connor, D. J. (2005). Environmental

risk analysis of farming systems in a semi-arid environment: effect of rotations and management practices

on deep drainage. Field Crops Research, 94(2-3), 257–271. doi:10.1016/j.fcr.2005.01.008

Jalota, S. K., Singh, S., Chahal, G. B. S., Ray, S. S., Panigraghy, S., Bhupinder-Singh, & Singh, K. B. (2010). Soil

texture, climate and management effects on plant growth, grain yield and water use by rainfed maize–

wheat cropping system: Field and simulation study. Agricultural Water Management, 97(1), 83–90.

doi:10.1016/j.agwat.2009.08.012

Jégo, G., Martínez, M., Antigüedad, I., Launay, M., Sanchez-Pérez, J. M., & Justes, E. (2008). Evaluation of

the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar

beet using the STICS soil–crop model. Science of The Total Environment, 394(2-3), 207–221.

doi:10.1016/j.scitotenv.2008.01.021

Justes, E., Mary, B., & Nicolardot, B. (2009). Quantifying and modelling C and N mineralization kinetics of

catch crop residues in soil: parameterization of the residue decomposition module of STICS model for

mature and non mature residues. Plant and Soil, 325(1-2), 171–185. doi:10.1007/s11104-009-9966-4

Klein, T., Calanca, P., Holzkämper, A., Lehmann, N., Roesch, A., & Fuhrer, J. (2012). Using farm accountancy

data to calibrate a crop model for climate impact studies. Agricultural Systems, 111, 23–33.

doi:10.1016/j.agsy.2012.05.001

Laux, P., Jäckel, G., Tingem, R. M., & Kunstmann, H. (2010). Impact of climate change on agricultural

productivity under rainfed conditions in Cameroon—A method to improve attainable crop yields by

planting date adaptations. Agricultural and Forest Meteorology, 150(9), 1258–1271.

doi:10.1016/j.agrformet.2010.05.008

Monzon, J. P., Sadras, V. O., & Andrade, F. H. (2012). Modelled yield and water use efficiency of maize in

response to crop management and Southern Oscillation Index in a soil-climate transect in Argentina. Field

Crops Research, 130, 8–18. doi:10.1016/j.fcr.2012.02.001

Moriondo, M., Giannakopoulos, C., & Bindi, M. (2010). Climate change impact assessment: the role of

climate extremes in crop yield simulation. Climatic Change, 104(3-4), 679–701. doi:10.1007/s10584-010-

9871-0

Pohlert, T. (2004). Use of empirical global radiation models for maize growth simulation. Agricultural and

Forest Meteorology, 126(1-2), 47–58. doi:10.1016/j.agrformet.2004.05.003

Robaina, F. G., Puebla, J. H., & Seijas, T. L. (2009). Factor de respuesta al agua de cultivos de interés agrícola

en suelo Ferralítico Rojo del sur de La Habana. Revista Ciencias Técnicas Agropecuarias, 18(3), 7–13.

Seijas, T. L., Puebla, J. H., Robaina, F. G., Lazo, G. C., & Durruty, Y. C. (2009). Eficiencia de un modelo de

simulación de cultivo para la predicción del rendimiento del maíz en la región del sur de la Habana. Revista

Ciencias Técnicas Agropecuarias, 18(3), 1–6.

Singh, A. K., Goyal, V., Mishra, A. K., & Parihar, S. S. (2013). Validation of CropSyst simulation model for

direct seeded rice-wheat cropping system. CURRENT SCIENCE, 104(10), 1324–1331.

Sommer, R., Wall, P. C., & Govaerts, B. (2007). Model-based assessment of maize cropping under

conventional and conservation agriculture in highland Mexico. Soil and Tillage Research, 94(1), 83–100.

doi:10.1016/j.still.2006.07.007

Soussana, J. F., Graux, A. I., & Tubiello, F. N. (2010). Improving the use of modelling for projections of

climate change impacts on crops and pastures. Journal of Experimental Botany, 61(8), 2217–2228.

doi:10.1093/jxb/erq100

Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European

Journal of Agronomy, 18(3), 289–307.

Stockle, C. O., & Nelson, R. (1994). Cropping systems simulation model user’s manual. Version. Retrieved

from http://www.sipeaa.it/tools/CropSyst/CropSyst_manual.pdf

Tardieu, F. (2010). Why work and discuss the basic principles of plant modelling 50 years after the first

plant models? Journal of Experimental Botany, 61(8), 2039–2041. doi:10.1093/jxb/erq135

Tingem, M., Rivington, M., Bellocchi, G., & Colls, J. (2008). Crop yield model validation for Cameroon.

Theoretical and Applied Climatology, 96(3-4), 275–280. doi:10.1007/s00704-008-0030-8

Tingem, M., Rivington, M., & Colls, J. (2008). Climate variability and maize production in Cameroon:

Simulating the effects of extreme dry and wet years. Singapore Journal of Tropical Geography, 29(3), 357–

370. doi:10.1111/j.1467-9493.2008.00344.x

Van Ittersum, M. K., Leffelaar, P. A., Van Keulen, H., Kropff, M. J., Bastiaans, L., & Goudriaan, J. (2003). On

approaches and applications of the Wageningen crop models. European Journal of Agronomy, 18(3), 201–

234.

Van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification

of agricultural input-output combinations. Field Crops Research, 52(3), 197–208.

Wang, T., Lu, C., & Yu, B. (2011). Production potential and yield gaps of summer maize in the Beijing-Tianjin-

Hebei Region. Journal of Geographical Sciences, 21(4), 677–688. doi:10.1007/s11442-011-0872-3

White, J. W., Hoogenboom, G., Kimball, B. A., & Wall, G. W. (2011a). Methodologies for simulating impacts

of climate change on crop production. Field Crops Research, 124(3), 357–368. doi:10.1016/j.fcr.2011.07.001

White, J. W., Hoogenboom, G., Kimball, B. A., & Wall, G. W. (2011b). Methodologies for simulating impacts

of climate change on crop production. Field Crops Research, 124(3), 357–368. doi:10.1016/j.fcr.2011.07.001

ANNEX 5

Presentations slides

10/25/2013

1

BioMA framework introduction - Training of Ms. Emilija Poposka

Jan 21, 2013, JRC, Ispra, Italy

The BioMA framework

Davide Fumagalli

On behalf of the Development Team

Institute for Environment and Sustainability

Joint Research Centre

1



Why BioMA framework

High level requirements:

• To increase the trasparency of the modelling solutions being built

compared to legacy code available, for each of the modelling solutions

being built;

• To increase the traceability of performance of each modelling unit used

in modelling solutions;

• To involve teams other than JRC without requiring them to commit to

a whole infrastructure they would not own and possibly would not use.

Summary: to maximize both reusability and openness

we chose to develop a simulation system based on framework-

indipendent components, both for model and for tool components.

2

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2



The framework structure: 3 layers

Configuration Layer: adding advanced functionalities

Composition Layer: modeling solutions from composition of model components

Model Layer: fine grained/composite models implemented in components



EC.JRC.MARS.ConfigurationLayer

EC.JRC.MARS.CompositionLayer

EC.JRC.MARS.ModelLayer

EC.JRC.MARS.BioMA

Layers namespaces

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3



Outline

● The Model Layer

● The Composition Layer

● The Configuration Layer

5



The Model layer (1)

6

• Set of classes to

code model/process

algorithms

(Strategies)

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4



The Model layer (2)

Each model is implemented as a strategy class which:

• Contains the algorithm/equation of the model

• Contains the definition of its own parameters

• Implements the test of pre- and post-conditions (inputs/params/outputs

validation)

• May use other classes (strategies) sharing the same interface

• Implements a scalable logging system

• Exposes the list of its inputs, outputs, simulation options, and parameters

• Exposes instances of concepts defined in a reference ontology

7



The Model layer (3)

Each variable used by the models (input, output or parameter) is

represented by a set of properties detailing a description,

max/min/default values, units, value type. (VarInfo class)

Each set of related variables is contained in a domain class which

represents a state of the simulated process (e.g. State of a plant)

All the strategies of the same library (component) share the same

domain classes, since the models share the same simulated physical

domain

8

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5



The Model layer (4)

Models are implemented using structural and behavioral design patterns

which foster extensibility and reusability:

• Composite (facilitating the use of composite and simple strategies)

• Strategy (allowing a context specific selection of models at run-time)

• Bridge (allowing the replacement of model components)

Models are made available with model and code documentation, and with sample projects for component reuse and extension.

9



Model layer tools

Applications are provided to:

• Explore component interfaces and domain classes: Model Component Explorer

• Generate the code of domain classes and parameter classes: Domain Class Coder

• Generate the code of model classes (strategies): Strategy Class Coder

• View/edit the values of the models parameters: Model Parameter Editor

10

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6



The BioMA model layer libraries:

11



Outline

● The Model Layer



12

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7



The Composition Layer

o From model layer we have a set of components. Each component

contains the model related to a specific agronomic process/model

(e.g. CropSyst, WOFOST, SoilW, Disease, weather data

provider,...)

o The components are independent of each other so they are

reusable

o Independent components must be linked together to create a

modeling solution



Composition example

3 components:

• Weather

• CropSyst model

• Frost damage model

=> One modeling solution

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8



Composition Layer purposes

• To define a cycle of simulation.

• Definition of the calling order of the components

• Calls at the time step chosen for communication across components

in the modeling solution (e.g. daily time steps or hourly time steps,

depending on the models)

To handle events to manage actions which are triggered not at

all time steps (e.g. agro-management events).

• To collect the models outputs into an aggregated model

output

• To return the aggregated output of the modeling solution (in

principle excluding persistence, which is part of the configuration,

hence context specific).

15



Composition Layer purposes (2)

o To provide to the higher level (Configuration level, Application)

aggregated informations (e.g. Modeling solution’s

inputs/ouputs/parameters/metadata)

o To provide to the higher level info about the components

used and how they are linked togheter

o To allow transfer of modeling options from the higher level

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9



What is a link between 2 components

17

TSource

component

Domain

classes

properties

TSource

component

Domain

classes

properties

TDest

component

Domain

classes

properties



Example of links between 2 components

TSource

component

Soil temperature

SoilTemperature States

SoilBorne

Exogenous

SoilTemperature Rates

SoilTemperature

Auxiliary

States

Rates

...

...

So

ilBo

rne

do

ma

incla

sses

So

il Te

mp

do

ma

in

cla

sse

s

SoilTemperature States Exogenous

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10



Example of links between 2 components

SoilTemperature States Exogenous

Soil temperature layer 1

Soil temperature layer 2

Layer[0].Soiltemperature

Layer[1].Soiltemperature

... ...



CLIC

To build a modeling solution the

composition layer provides a visual tool

to assist creating code units to be

compiled (CLIC: Composition Layer

Interactive Coder).

20

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11



The sample modeling solution (CropSyst)

Agronomic components:

CropSyst Potential

CropSyst Water Limited

Soil Water content

Soil Runoff Erosion

Soil Temperature

SoilBorne

Auxiliary components:

Weather data provider

Soil data provider

Agromanagement



Soil Borne

Weather

CropSyst

potential

SoilWater

content

Soil temperature

Soil dataAgromanagement

CropSyst Water

Limited

Soil runoff erosion

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12



Outline

● The Model Layer



23



Configuration: Needs

Adapt the composition layer to several execution conditions:

• Different sources of inputs data (data providers)

• Different kinds of output persistence

• Different kinds of executions (a modeling solution can be run

iteratively to simulate widely distributed areas, for calibration,

for inspecting results at field scale, ...)

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13



Configuration Layer RequirementsThe BioMA framework interacts with «Configurable» objects

A Configuration must allow:

To be validated

To be saved and reloaded later

To notify changes in its status (MVC applications)

To be composed in complex configurations

To be defined in terms of items to fill

All

All these are GUIagnostic

requirements



Configuration Layer Requirements (2)

• Verify items validity with respect to the environment of

execution.

• Save a configuration for later reloading.

• Create recursive configuration structures, in case one of the

items constituting the configuration needs in turn to be

configured.

• Support callback functions when the status of a configuration

changes, to refresh views attached in a Model View Controller

architecture.

26

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14



BioMA applications summary

2 phases:

1. Modeling solution development:

A modeler/programmer creates a modeling solution starting from model

layer by using: SCC, DCC, and other model layer tools

The modeler/programmer creates the composition/configuration layer

by using CLIC application

2. Modeling solution use:

The modeler loads the modeling solution into BioMA Spatial or BioMA

Point, and run it

The modeler loads the modeling solution into Optimizer or LUISA to

perform a parameters calibration or a sensitivity analysis

27



BioMA Point

• To run a modeling solution on a specific location/time interval

with an high level of detail on the modeling solution’s

components.

• Used mainly for the first tests of the modeling solution.

28

10/25/2013

15



BioMA Spatial

• To run a modeling solution on a many locations/time intervals.

• Used mainly for extensive runs, saving the results on a DB.

• Plugins to analyze simulation results (GDD and MDV).

29



GDD

Graph and Data Display: tool to create graphs on simulation results

30

10/25/2013

16



31

Possibility to create custom graphs

GDD



10/25/2013

17



MDV: Map Data Visualizer



Optimizer

Modeling solution’s parameters calibration

34

10/25/2013

18



LUISA

Modeling solution’s parameters sensitivity analysis

35

10/25/2013

1

BioMA framework introduction – BASAL Kick off meeting

Mar 26, 2013, JRC, Ispra, Italy

The BioMA framework

Davide Fumagalli




1



Agronomical model

CROP GROWTH MODEL

Yield

IncomingSolar Radiation

Mean DailyTemperature

Precipitations Management practices

Soil type

Crop variety parameters

Biomass

Crop calendar

LAI/GAI

10/25/2013

2



Modular model (1)

• Model made by different

components

• Each component represents a part

of the physical model

• Some components can be optional

• Components can contain different

approaches to calculate the same

outputs, according to the available

inputs

3



Modular model (2)

• Components must be reusable at

any spatial/time scale

• Independent components are linked

together to create a modeling

solution

4

10/25/2013

3



Composition example

3 components:

• Weather data provider

• CropSyst model

• Frost damage model

=> One modeling solution

Weather data

provider

CropSyst model

Frost damage model



The BioMA platform components:

6

To be

changed

10/25/2013

4



BioMA platform

BioMA platform= set of software tools and

applications to manage modular models.

• Components of modular models must

communicate => rules needed!

• High transparency/traceability of

performance of the models (compared to legacy

code available)

• Involve teams other than JRC without requiring

them to commit to a whole infrastructure they

would not own and possibly would not use.

Summary: to maximize both reusability

and openness

=>Modular Software Architecture

7



Soil borne pathogens

Weather data

CropSyst

potential

Soil water

content

Soil temperature

Soil dataAgro-management data

CropSyst water

limited

Soil runoff erosion

Real composition example (CropSyst modeling solution)

10/25/2013

5



Demonstration of BioMAProduction of spatialized agro-climatic indices

BioMAModelling Solution:

ClimIndices

Generation of weather data

Maps of Indices



Demonstration of BioMAEUROCLIMA project

10

10/25/2013

6



Demonstration of BioMAEUROCLIMA: production of continental scale crop simulation

• Crop growth simulation:

• Yield at 3 production levels (potential, water-limited, disease-limited)

• 4 crops (soy, wheat, maize, rice)



Demonstration of BioMA:Project PESETA: Climate change adaptation study

12

D=delay in sowing, C=cycle length (days)

10/25/2013

7



Data required (1)

Agro-management

data:

• Crop masks

• Sowing date

• Harvest date

• Irrigations

• Other…

13



Data required (2)

Crop data:

• Crop varieties

distribution

• Variety

parameters

14

Rapeseed varieties in CGMS

10/25/2013

8



Data required (3)

Weather data (daily):

• Mandatory: temperatures

(max and min), rainfall

• Useful: wind speed,

evapotranspiration,

humidity, solar radiation,

• Historic and future

forecasted weather

15



Data required (4)Soil data:

• Different soil water content

calculation approaches.

Depending on the chosen

approach, we need different

soil parameters.

• In our most current applications we

need soil water content at field capacity

and at wilting point

16

10/25/2013

9



Data required (5)The resolutions of input data could be different. The simulations will

be performed at the highest level of resolution (intersection)

17

Weather dataSoil data Simulations resolution



Software tools

18

• BioMA Spatial• Map Visualizer• GDD• Optimizer• LUISA

10/25/2013

10



BioMA Spatial

• Run a modeling solution on a many locations/time intervals.

• Used mainly for extensive runs, saving the results on a DB.

• Plugins to analyze simulation results (GDD and MDV).

19



MDV: Map Data Visualizer

10/25/2013

11



GDDGraph and Data Display: tool to create graphs on simulation results

21



Optimizer

Modeling solution’s parameters calibration

22

10/25/2013

12



LUISA

Modeling solution’s parameters sensitivity analysis

23



Hardware/software requirements• The BioMA applications run on

Windows OS PCs

• At least 4 GB ram. More for parallel

simulations.

• Database to store input data and

simulation results:

• Oracle, SQLServer, PostgreSQL,…

• Web Server to host the website

• Hardware capacity to be estimated according to actual needs

10/25/2013

13



Human resources• Analysts/agronomists to run the

model and evaluate simulation

results

• IT professional to modify the code

of models and tools according to

specific needs

• IT system administrator to

manage the servers

1

BioMA – Biophysical Models Application

Overview

Fabien Ramos

On behalf of the development Team

25 October 2013 1

BioMA is a platform that aims to give support in

parameterizing, running and analyzing crop

model simulations.

What is BioMA ?

2

A modular framework to implement complexmodels.

A Graphical User Interface to develop and run modelling solutions.

A library of models already implemented.

Tools dedicated to calibration and sensitivityanalysis.

What is BioMA ?

What is BioMA ?

3

A Modular Architecture

A modular conceptualization of models allows:

An easier transfer of research results to operational tools;

The comparison of different approaches;

More rapid application development;

Re-use of models of known quality;

Independent extensibility by third parties;

Avoiding duplication.

A modulararchitecture

4


Model layer: fine grained/composite models (strategies) implemented in components

Composition layer: modeling solutions from model components

Configuration layer: adapters for advanced functionalities in controllers

Applications: from console to advanced MVC implementations


5

Model layer (strategies)

A strategy :

Contains the algorithm/equation of the model

Contains the definition of its own parameters

Implements the test of pre- and post-conditions (inputs/params/outputs validation)

May use other classes (strategies) sharing the same interface

10

Model layer (strategies)

6


Composition layer (Modelling Solutions)

A composition layer allows linking model components to build modelling solutions;

A modelling solution is developed and used for a specific purpose (e.g. a “crop model” in which we link crop, soil water and other components to simulate water

limited production of crops);

The composition layer include Time and Event handling.

7

Water

Erosion

Temperature

SOIL

Weather

Agro-management Diseases

Composition layer (Modelling Solutions)

Modelling Solutions

Water

Erosion

Temperature

SOIL

Weather

Agro-management

Modellingsolution

Diseases

8


The Configuration layer :

Allows setting configuration values at run time;

Allows validating configurations;

Serializes/deserializes configurations;

Allows running modeling solutions;

Triggers events when the configuration is changed, to be used by a

controller in a MVC application;

Does not have any dependency from the composition layer;

Allows iteration (space, time, optimization…);

Requires simple implementation to build adapters.

Configuration layer

9

Water

Erosion

Temperature

SOIL

Weather

Agro-managementModellingsolution

Diseases

Configuration layer

XML

WeatherDB

SoilDB

The strategies are the building blocs of the models, they belong to the

model layer. They can be simple or composite when they call other

strategies.

A component is a library of strategies dedicated to model a specific

process. For instance the strategies used for the modelling of soil erosion

and runoff are included in the component SoilRE.

A modelling solution is obtained when the components are connected and

possibly configured to run a simulation. The terminology « modelling

solution » is used indifferently at composition layer and configuration

layer.

Models can refer to strategies or modelling solutions.

Terminology

10

Graphical User Interface

GUI

11

GUI

GUI

12

Models ready for BioMA

What is BioMA ?

Crop models available

CropSyst (Generic crop/cropping systems simulator)

WOFOST (Generic crop simulator)

WARM (Rice simulation)

STICS (Generic crop / Grassland simulator)

CANEGRO (Sugarcane)

Plant growthmodels

13

and some other components…

PotentialDiseaseInfection (Airborne plant diseases)

PotentialSoilDiseaseInfection (Soilborne plant diseases)

MYMICS (Mycotoxin maize)

GrainQuality (Currently rice)

ClimIndices (Climatic indices)

Run a simulation

ClimIndices is a component containing routines to calculate

weather indicators from multi-year series of daily

weather data.

Basic indicators are computed as simple statistics on weather

inputs (yearly series of daily precipitation, maximum and

minimum air temperatures, incoming solar radiation, and

reference evapotranspiration).

Other indicators are derived, grouped into six classes: dates,

counts, thermal sums, water, waves, indices.

Climate indices

14

The libraries currently available

27

Weather libraries

• AirTemperature, EvapoTranspiration, LeafWetness• Climate indices• Weather Generation (ClimGen, CLIMAK) • …

Plant libraries

• Generic crop Simulation (CropSyst, WOFOST)• Pasture (STIC)• Rice (WARM)• SugarCane (CANEGRO)• …

Soil libraries

• Soil water runoff and erosion• Soil water redistribution (cascading,

FiniteDifferences)• Soil surface and profile temperature• Soil Nitrogen• Pedotransfer functions• …

Agriculture management

• Rule based modelling

StressesAbiotic• Heat damage• Frost kill• Rice cold shocks• Lodging• …

Biotic• Generic air-borne diseases• Generic soil-borne diseases• CornBorer simulator• …

Tools

What is BioMA ?

15

IMMA: A tool for comparing simulation results to reference data

(e.g. time series)

Optimizer: A tool for calibration and parameter optimization

LUISA : A tool for sensitivity analysis (Morits, Sobol …)

Tools

Documentation

What is BioMA ?

16

Framework documentation

Weather

Air Temperature Rain

17

Leaf Wetness Evapotranspiration

Weather

Soil

18

Models ready for BioMA

Soil

Stresses

19

To run an existing modelling solution and look at the results;

To implement some strategies (Model layer);

To build a new modelling solution (Composition layer);

To configure a modelling solution and run it (Configuration layer).

Tasks of the week

Thank you

3825 October 2013

Contact :

[email protected]

Documentation:

http://bioma.jrc.ec.europa.eu/bioma/help/

http://agsys.cra-cin.it/tools/default.aspx

http://bioma.jrc.ec.europa.eu/bioma/help/

http://agsys.cra-cin.it/tools/default.aspx

25/10/2013

1

Training in BioMA, 2-6 Sept 2013, JRC, Ispra, Italy

Principles of agricultural modelling

Maurits van den Berg




Objective:

To present a brief introduction of modelling and systems approaches with special attention for their role in agricultural decision support.

25/10/2013

2


Contents

• Types of models (for decision support)

• Dynamic, numerical, computer-based, mechanistic, simulation models

• Crop growth models

• The role of models in agricultural decision making

• The use of models in agricultural decision making


Objective:

To present a brief introduction of modelling and systems approaches with special attention for their role in agricultural decision support.

25/10/2013

3


Mental models

? = f ( ? , ? , ? )

Types of models (for decision support)


Flow Chart for Decision Support

DOES THE

DEVICE WORK

YES NO

NO

NO

NO

NO

YES

YES

YES

NO

PROBLEM

DON’T MESS

WITH IT

DID YOU

MESS

WITH IT

WILL YOU

GET

FIRED?

TRASH IT

CAN YOU BLAME

SOMEONE ELSE?

HIDE ITDOES ANYONE

KNOW?

YOU

POOR

IDIOT

YOU

IDIOT

YES

25/10/2013

4


(Computer based) mathematical models

Static - e.g. models for fertiliser recommendation based on soil/plant analysis

Dynamic - e.g. crop growth (simulation) models


Strengths and Weaknesses

Type Versatility Speed Ease of

use

Objective /

reproducible Quantified Human

aspects Dynamic

Mental models ++ ++ / -- ++ - - ++ +

Decision trees - 0 + ++ - - -

Computer-based

dynamic + + / ++ - ++ ++ - ++

25/10/2013

5


Conclusions

• All our decisions are supported by models

• Computer-based models are just one specific type (or family) of models

• We must have good reasons to use them


Yt+ t = Yt + Ryt * t

Yt+ t : State variable at time t + t

Yt : State variable at time t

Ryt : Rate of change

t : Time interval

Dynamic numerical computer-based models

25/10/2013

6


Examples:

Falling apple

Algae growth in pond

Crop growth models





t : Time interval



Analytical solution:

Yt = ½ g*t2 + v0tFalling apple

Yt+ t = Yt + vyt * t

Yt+ t : Vertical distance at time t + t

Yt : Vertical distance at time t

vyt : Speed at time t, position Y

t : Time interval

Falling apple

gravity.xls

algae.xls

Sucros_1b_Eng.ppt

gravity.xls

25/10/2013

7



Yt+ t = Yt + (gyt - dyt ) * t

Yt+ t : Algae biomass at time t + t (g.l-1)

Yt : Algae biomass at time t

gyt : rate of new biomass growth (g.l-1.d-1)

dyt : death rate (g.l-1.d-1)

t : Time interval (d)



Crop growth models

Main state variables

Development stage

Leaf area index (LAI)

Leaf biomass

Stem biomass

Storage organs (e.g. grain) biomass

Root biomass

Main rate variables

Development rate

Photosynthesis

Respiration (growth, maintenance)

Rate of LAI change

Change in biomass of leaves, stems, storage organs, roots

algae.xls

25/10/2013

8


Photosynthesis

Respiration

Leaf area index

Genotype

coefficientsTemperature

Radiation

Assimilates ConversionPartitio-

ning

Leaves

Stems

Repr. organs

Roots

Development rate

Development stage

(Daylength)

Potential transpiration

Actual transpiration


Water balance module


Soil water content (at different depths)

Main rate variables

Infiltration

Transpiration (crop water uptake)

Evaporation

Runoff

Drainage

25/10/2013

9


Windspeed

Temperature

Radiation

Humidity

Potential

evapotranspiration

Actual

transpiration

Actual

evaporation

Drainage

Soil water

Potential

evaporation

Infiltration

Runoff

Field capacity

Permanent wilting

point

Potential

transpiration

Leaf area index

Precipitation


Other modules can be added

For example

Nutrients (N, P, K, micro)

Pests, Diseases

Weeds

Pollutants

25/10/2013

10


Características

da cultura

Irradiação, temperatura

Water limited

med. fitossanitárias

adubação

irrigação

Disponibilidade

de N, P, K Lim. nutrient.

Precipitação

Relações solo-

água, declive,

enraizamento

Lim. água

Potencial


adubação

irrigação


adubação

irrigação

Incidência e

influência de

doenças,

pragas etc. med. fitossanitárias

adubação

irrigação

Atual

Dados requeridos Nível de produção

Mo

de

loA

ná

lise

“Yield gaps”:


Conclusions

• Numerical models are composed of simple building blocks

• Complexity is derived from how these are assembled

• Critical factors for model quality:

• What factors are accounted for

• How

• Input data

• Correct initialisation

• Parameterisation

• Time step compatible with system dynamics

• Balanced complexity (we smplfy)

25/10/2013

11


Operational and tactical (1 day – 1 season)

Strategic (1 season – ca. 10 years)

Exploratory use for policy making (long term)

Examples of questions that can be addressed

The role of models in decision-making


Operational and tactical (1 day – 1 season)

The best time of planting and harvesting?

Beginning and end of milling season?

How much grain can we sell at contract?

Irrigation: how? when? how much?

How much nitrogen and when?

Risk of pests?

Yields expected world-wide? Impact on prices?



25/10/2013

12


Strategic (1 season – ca. 10 years)

What type of irrigation system (if at all)?

Environmental impacts? Can they be improved?

What yields are attainable?

How?

How does that compare with actual yields?




Exploratory use for policy making (long term)

Can we feed the world?

Impact of climate change?

What are the implications for water use

Do we need adapted varieties?

Do we need to adapt land use systems

How can we cope with changes in

Demography

Bioenergy policies

global trade…?



25/10/2013

13


X Operational and tactical (1 day – 1 season)

V Strategic (1 season – ca. 10 years)

V Exploratory use for policy making (long term)




X Operational and tactical (1 day – 1 season)

V Strategic (1 season – ca. 10 years)

V Exploratory use for policy making (long term)



25/10/2013

14


Example: Results of questionnaires for SA sugarcane industry

Questions inspired by:

Technology Acceptance Model (TAM, Davis, 1989):

Perceived utility (PU)

Helps increase profits?

Helps save time?

Helps to provide better advice?

Makes work more enjoyable?

Helps increase understanding?

Perceived ease of use (PEOU)

Easy access to system (e.g. by internet, cell-phone)

Menu structure clear and straightforward?

Input data easily available?

Output easy to interpret?

Quick response to queries?


1 2 3 4 5 6 7

I don’t like computers to “think” for me

Don’t help making better assessments or provide better advice

In my job, I don’t need DSPs

Results / outputs are not practical

Results / outputs are unclear

Results / outputs do not correspond to my needs

Output can be inaccurate or incorrect

Too much focus on scientific issues

The issues addressed are not relevant for my job

Too many bugs in the software

They are like a black box

Difficult to operate

Own way to address issues works just as well

The DSPs are not properly tested in practice

Difficult / impossible to find correct input data

Menu structure poorly designed or too complicated

Impractical to use in the field

Lack of uniformity in menu, data formats etc

Lack of support to users

Poor dissemination and marketing

Lack of training opportunities

Unreliable or slow internet access

Lack of documentation (user manual etc.)

Very strong barrierNo barrier at all

Perceived barriers to DSS adoption(Average response of 27 SASRI Researchers & EO’s, Oct 2006)

Lack of ease of use

Lack of (potential)utility

25/10/2013

15


Critical factors for adoption of model-based decision support systems (DSS)• DSS should respond to imminent needs or pressure to

change practices

• DSS should provide ‘answers’ that are difficult to obtain without it

• Scope, complexity and format of use must be tailored to user needs and capacity (rather than developers’ fancy);

• Broad institutional support is required for DSS oriented research, development, implementation and maintenance;

• Post-development user support and marketing system must be in place.


Potential contribution of systems modelling

to resolve issue

(Po

ten

tial)

im

pact

of

issu

e o

n

ind

ustr

y v

iab

ilit

y

++

++

+

+ ++ +++

+

Supply chain

optimisation

Exploratory

assessments

(inc Alternative

products)

Environmental

impacts

Field

management

Weeds, pests

diseases

Important to industry; systems modelling will have a prominent role in

resolving the issue

Systems modelling will have less impact, but research is needed to attain

progress on issues with a higher impact

Water issues

Crop

forecasting

Potential contribution of systems modelling

to resolve issue

(Po

ten

tial)

im

pact

of

issu

e o

n

ind

ustr

y v

iab

ilit

y

++

++

+

+ ++ +++

+

Supply chain

optimisation

Exploratory

assessments

(inc Alternative

products)

Environmental

impacts

Field

management

Weeds, pests

diseases


resolving the issue




resolving the issue



Water issues

Crop

forecasting

What determines utility?(Outcome SASRI workshops, 2005)

25/10/2013

16


Conclusions

• Computer-based agricultural models have a role to play in operational, technical and strategic decision making as well as in exploratory studies

• So far they are underutilised for operational decision support

• Critical adoption factors need to be taken into account during all stages of development and after release

25/10/2013

1


WOFOST

Maurits van den Berg




WOFOST

Development started in early 1980’s as part of WOrld

FOod Studies initiative; released in 1988

Developed by Wageningen University and Research

Centre (current maintenance Alterra)

http://www.wageningenur.nl/en/Expertise-

Services/Research-Institutes/alterra/Facilities-

Products/Software/WOFOST/Documentation-

WOFOST.htm

2

http://www.wageningenur.nl/en/Expertise-Services/Research-Institutes/alterra/Facilities-Products/Software/WOFOST/Documentation-WOFOST.htm

25/10/2013

2


WOFOST

Main characteristics

Generic model, many crops can be (and are) simulated

Point model

Dynamic, numerical integration; one-day time step

Simulates potential growth and water-limited growth

Process descriptions based as much as possible on

universally valid bio-physical laws

Differences between crops expressed in “easily

interpreted” model parameters

Used as starting point for many other models

25 October 2013






t : Time interval


25/10/2013

3



Development stage

Leaf area index (LAI)

Leaf biomass

Stem biomass

Storage organs (e.g. grain) biomass

Root biomass

Main rate variables

Development rate

Photosynthesis

Respiration (growth, maintenance)

Leaf senescence, root senescence

Change in biomass of leaves, stems, storage organs, roots


Photosynthesis

Respiration

Leaf area index

Genotype


Radiation


ning

Leaves

Stems

Repr. organs

Roots

Development rate

Development stage

(Daylength)



25/10/2013

4


7


Photosynthesis & biomass growth

8

ΔW/Δt = Ce * (A-Rm)

ΔW/Δt = crop biomass growth rate

A = Gross assimilation rate (photosynthesis)

Rm = maintenance respiration rate

Ce = conversion efficiency (from sugars to biomass)

25/10/2013

5


Photosynthesis & biomass growth

9

ΔW/Δt = Ce * (A-Rm)

Gross assimilation rate (A) depends on light interception f(radiation, LAI, ke) and light use efficiency; calculated by Gaussian integration;

• Different relations for C3 and C4 crops

Maintenance respiration rate (Rm, kg/(kg.day) depends on crop composition (proteins) and temperature

Conversion efficiency (Ce) incorporates growth respiration and mass conversion aspects

• Depends on composition of new biomass


Growth of individual crop components

10

WOFOST distinguishes 4 crop components:

• Roots

• Leaves

• Stems

• Storage organs (reproductive or vegetative)

ΔW/Δt is first partitioned between roots and shoots

Then, new biomass allocated to shoots is further partitioned among leaves, stems and storage organs (because easier measured)

Partitioning factors are governed by development stage (in some versions also influenced by water stress)

For roots and shoots, senescence is taken into account.

25/10/2013

6


Phenological development

11

0 = emergence, 1 = anthesis, 2 = physiologic maturity

Development rate mainly driven by temperature (thermal time concept; crop dependent base-temperature)

Corrections are made: at high temperatures for daylength in case of photoperiod sensitive crops In case of water stress (acceleration)

Development stage mainly impacts on dry matter partitioning among roots, shoots, storage organs



12




Development stage mainly impacts on dry matter partitioning among roots, shoots, storage organs

25/10/2013

7



13




Development stage mainly impacts on: dry matter partitioning among roots, shoots, storage

organs maximum leaf CO2 assimilation rate


Leaf area development (LAI)

14

LAI is simply calculated each day as:

Leaf biomass (kg/m2)--------------------------------Specific leaf mass (kg/m2)

Specific leaf mass is crop specific parameter (constant(?))

In some versions, early LAI extension is calculated as temperature dependent (sink dependent vs source dependent).

25/10/2013

8


15


Photosynthesis

Respiration

Leaf area index

Genotype


Radiation


ning

Leaves

Stems

Repr. organs

Roots

Development rate

Development stage

(Daylength)



25/10/2013

9


Crop water relations

Impacts on crop growth

ETact/Etpot directly impacts on photosynthesis rate

Can have an effect on biomass partitioning and physiologic ageing




Soil water content (at different depths)

Main rate variables

Infiltration

Transpiration (crop water uptake)

Evaporation

Runoff

Drainage

25/10/2013

10


Windspeed

Temperature

Radiation

Humidity

Potential

evapotranspiration

Actual

transpiration

Actual

evaporation

Drainage

Soil water

Potential

evaporation

Infiltration

Runoff

Field capacity

Permanent wilting

point

Potential

transpiration

Leaf area index

Precipitation



Infiltration calculated from rainfall/irrigation and infiltration capacity

Potential transpiration (crop water uptake) and evaporation (from soil): Penman Monteith or as weather input

Actual transpiration: drops below potential if

• soil water content drops below critical level; 0 at permanent wilting point

• Soil air content below critical level (can be switched off)

Runoff: Non-infiltrating incoming water that cannot be stored at the surface

Drainage: fraction of soil water in excess of field capacity

25/10/2013

11



Soil modelled as three layer profile:

• 0 – actual rooting depth

• Actual – potential rooting depth

• > potential rooting depth

Constant vertical root extension until maximum rooting depth is reached

Simple module can be switched on to account for capillary rise.


Input data

As others model, 4 groups of input

Many crop parameters given as “AFGEN” functions (tabular)

22

- Rainfall- Min and max temperature- Radiation (Mj/m²/day)-Wind speed (m/s)- Vapor pressure (mbar)- Evapotranspiration (mm)

- Sowing / emergence date

- Irrigations- (Harvesting date)

- Rootable depth- Initial soil water content- Water content at fieldcapacity and wiltingpoint)

- Parametersgoverningrelations betweentemperatrure and development;

-Partitioningcoefficients

-SLW-Light extinction coefficient

-Root growth-Critical soil water content

-….

Climate (daily) Agromanagement Soil Crop

25/10/2013

12


Conclusions

23

Strengths:- Sophisticated calculation of photosynthesis and biomass

accumulation, based on universal principles- Model based on universal principles should make it universally

applicable and facilitate parameterization- Several decades of continued support and development (…)- Well documented

Weaknesses:- Several constants and fixed relations are not really constant- Afgen functions not elegant- Water balance (and soil in general) strongly simplified

compared to other model components- User community rather restricted- Lacks simulation of nutrients, pests etc- Lacks management options

25/10/2013

1

WARM model:overview and applications

Simone Bregaglio and Giovanni Cappelli

On behalf of the development team

University of Milan, CASSANDRA (Centre for Advanced Simulation Studies AND Researches on Agroecological modelling), [email protected]

BASAL project training course – JRC Ispra, Italy – 03 September 2013


BioMA-WARMlinks

o WARM 2 is a component-based application developed following the same software architecture of BioMA.

o The same software components implemented in many BioMAmodelling solutions are re-used and implemented in WARM 2.

o Not only algorithms, also development tools are used in WARM 2 application.

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Some examples…Biophysical models

o CropML library: simulation of crop growth and development;o SoilW library: simulation of water dynamics in the soil profile;o Diseases libraries: simulation of the impact of a generic fungal disease;o AbioticDamage library: simulation of abiotic stresses on crop production.

Development toolso Model Parameter Editor: to modify models parameters;o Model Component Explorer: to explore strategies and domain classes;o Graphic Data Display: to view and evaluate simulation results;o Agromanagement Configurator: to define agromanagement practices.

BioMA-WARMlinks


o WARM 2 represents a proof of concept of the suitability of the BioMAframework to provide reusability of discrete model units as well as advanced supporting tools.

o Given the full compatibility with BioMA architecture, WARM 2 aims at providing a “virtual paddy rice field”, in which users with different backgrounds can, for example: Test the impact of different management or weather scenarios; Test alternative approaches for the simulation of the same

biophysical process; Test the impact of the introduction of new genotypes in terms of

quantitative and qualitative production; Perform long term simulations with synthetic weather series

according to different climate change scenarios;

BioMA-WARMlinks

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Outline

Introduction

Description of key processes

Application results

Conclusions

The practical exercise


Outline

Introduction


Application results

Conclusions


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4


Introduction

Given the paramount importance of rice crop as a staple food at a global level, many crop simulators were developed specifically to reproduce the peculiarities of paddy rice agricultural systems.

As an example, AgMIP rice team is composed by 13 rice models (e.g., Oryza2000, CERES-rice, DNDC-Rice, RiceGrow, GEMRICE).

Why another rice crop model?

Many aspects strongly affecting rice yields in temperate climate are not properly considered by existing approaches, like:o floodwater effect on vertical thermal profile;o spikelet sterility due to pre-flowering temperature shocks;o blast disease;o peculiar hydrology of paddy rice when soil presents high hydraulic

conductivity;o grain quality characteristics.


Introduction

The development of WARM (Water Accounting Rice Model) started in 2005, aiming at a model:

o able to take into account all the key processes affecting rice crop quantitative and qualitative productions (biotic and abiotic stresses, grain composition, micrometeorology);

o presenting a balance between the level of detail adopted to reproduce the biophysical processes related to crop growth and development (e.g., phenology, biomass accumulation, assimilate partitioning)

o showing a marked usability, in terms of intuitiveness of the graphical user interface and of capability of setting up a customized modelling solution, according to the user needs.

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Outline

Introduction


Application results

Conclusions



Time step:

o The simplification of using daily temperatures holds only when almost all the corresponding hourly temperatures fall within the range the crop is adapted to;

o If hourly temperatures in the warmest or coldest hours of the day are outside that range, the use of average daily temperature would lead to errors in the estimation of the temperature effect on photosynthesisand accumulation of thermal time;

o for sure the use of daily temperatures is a simplification that proved to work, at least under conditions the crop is adapted to but we need to consider scenarios for which the crop could be less adapted (climate change issues, new environments)

Development & growth

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6

Time step:

Tn

DayDay length = 12; Tmin = 24; Tmax = 43; Tavg=33.5

Crop Tn = 12;Tx = 43; To= 33;

To

Tx




Tn

To

Tx

Tn

To

Tx

f(t) = 0.998hour T (°C) f(t)

8 31.04 0.969

9 33.50 0.998

10 35.96 0.913

11 38.25 0.701

12 40.22 0.395

13 41.73 0.068

14 42.68 0.000

15 43.00 0.000

16 42.68 0.000

17 41.73 0.068

18 40.22 0.395

19 38.25 0.701

20 35.96 0.913

f(t) avg = 0.471

DAILY TIME STEP

HOURLY TIME STEP

Tavg=33.5 °C


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Phenology:

o Crop development is based on the thermal time accumulated (Yin function).

o The degree-days are converted into a decimal code to standardize development stages (phenophases are not explicitly determined).

Sowing: 0.00 Emergence: 1.00 Beginning of tillering: 1.25 Mid tillering: 1.35 Panicle initiation: 1.60 Full heading: 1.90 Full flowering: 2.00 Full grain filling: 2.50 Physiological maturity: 3.00 Harvestable: 4.00



Photosynthesis:

𝐴𝐺𝐵 = 𝑅𝑈𝐸𝑎𝑐𝑡 ∙ 0.5 ∙ 𝑅𝑎𝑑 ∙ 1 − 𝑒−𝑘∙𝐿𝐴𝐼

o AGB = biomass rate (g m2 d-1)o RUEact = actual radiation use efficiency (g MJ-1)o Rad = global solar radiation (MJ m-2 d-1)o k = extinction coefficient for solar radiation (-)o LAI = leaf area index (m2 m-2)

Maximum radiation use efficiency is influenced by

𝑅𝑈𝐸𝑎𝑐𝑡 = 𝑅𝑈𝐸𝑚𝑎𝑥 ∙ 𝑇𝑙𝑖𝑚 ∙ 𝑅𝑎𝑑_𝐹 ∙ 𝐷𝑉𝑆_𝐹 ∙ 𝐶𝑂2𝐹

temperature radiation senescence carbon dioxide

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Partitioning of assimilates is driven by a single parameter, i.e., the fraction of photosynthates allocated to leaves at emergence.

1 1.7 2 2.5 3



Leaf area evolution:

o Specific leaf area (m2 kg-1) varies from sowing to tillering. o Leaf area index (LAI) increment is the product of SLA and the rate of aboveground biomass partitioned to the leaves. o Senescence is computed by subtracting dead LAI to the total one. Eachday a LAI unit is emitted and a degree days threshold is assigned (leaf life)

1 1.35

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9


Micrometeosimulation

o Quantification of the effect of flooding water on development and growtho Temperature is one of the most relevant driving variables for crop modelso Uncertainties in its estimation decidedly lower the degree of adherenceof the model to the real system

0

5

10

15

20

25

30

35

minutes

Tem

pera

ture

(°C

)

0

5

10

15

20

25

30

Wate

r le

vel

(cm

)

air T (170cm; °C)

air T (20 cm; °C)

water T (°C)

water level (cm)

Close canopy stage


Soil hydrology

o UNIMI.SoilW component is linked to CropML component in order to simulate water dynamics in the soil profile;

o Soil can be divided in a variable number of layers of different thickness, each one with its properties (e.g., texture, hydraulic conductivity)

o Three levels of users were identified:

Expert: he/she directly composes – on the base of his/her experience and of the specific context – different simple strategies to build a complete model

Intermediate user: he/she can chooses composite strategies implementing different complete models (e.g., EPIC, CropSyst, CERES)

Beginner (or expert in other sub-domains): it is possible to select a context strategy: the component autonomously select the configuration of hydrological model more suitable for the conditions explored (e.g., availability of data)

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Is Lambda (water extraction parameter) available?

Yes

No

Is water potential at leaf wilting available?

Yes

No

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SoilW


Yes

No


Yes

No

SoilW


Yes

No


Yes

No

25/10/2013

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SoilW

Is potential evaporation available?

Yes

No

25/10/2013

13

SoilW

Is potential evaporation available?

Yes

No

SoilW

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Abiotic stresses:spikelet sterility

o The frequency of extreme events is expected to change (IPCC, 2007)o Temperature related shocks will surely change their impact, with different effects in different areas


Abiotic stresses:spikelet sterility

headDay

headDayi

GDDGDD

h

hid

i

eTTSterility22

224

1

,

2

211

2

1

Fattore campana

Fattori orariHourly factors Bell factorTime (hours)

T (°C)

sterilitythreshold

0

0.2

0.4

0.6

0.8

1

497 597 697 797 897 997 1097

Developing stage (GDD)

bell

facto

r

high bell factor

low bell factor

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Biotic stresses:fungal diseases

o Blast (Magnaporthe oryzae) and brown spot (Bipolaris oryzae)diseases can be simulated by WARM 2.

o A generic framework for airborne fungal plant diseases was linked to the WARM model (fully compatible with BioMA architecture).

o It simulates the evolution of the disease with an hourly time step, considering the following components:

infection, incubation, latency, infectiousness, sporulation, and spore dispersal.

o Each is simulated as a function of meteorological variables and biological significant parameters, specific for each host-pathogen couple


0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30 35

temperature (°C)

f(T

)

Topt

TminTmax

Healthy OldVisible InfectiousLatent

Incubation

Latency

Infection

Sporulation

Dispersal &Catch

New infections


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Grain quality

o Process-based models are used to forecast the potential impacts of climate change on agricultural systems and to define adaptation strategies (White et al., 2011).

o Crop quality represents an aspect scarcely investigated in crop modelling activities (Porter et al., 2005), but is fundamental to assess: the economic and nutritional value (Koutroubas et al., 2004); food security levels.

o Many authors agree in forecasting a decay in crop quality underclimate change conditions (Oury et al., 2003; Erda et al., 2005; Porter et al., 2005;

Slingo et al., 2005; Terao et al., 2005; Da Matta et al., 2010; Yamakawa et al., 2010; Okada et al, 2011;Shah et al., 2011; Wanga et al., 2011; Fernando et al., 2012; Madan et al., 2012; Ning et al., 2012;Weigel et al., 2012; Högi et al., 2013 ).

o Superior grain quality of rice has become a key target in current riceproduction (Cheng and Zhu, 1998; Huang et al., 1998; Li et al., 2005; Tesio et al.,

2013).

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Grain quality

o Variables with effect on functional and sensory properties

o Variables affecting the appearance and milling quality

o Variables with impact on milling quality

Starch concentration (Chen et al., 2011);

Protein content (Li et al., 2005; Lanning et al., 2012);

Amylose content (Chen et al., 2005; Li et al., 2005; Lanning et al., 2012; Liu et al., 2013);

Total lipid content (Lanning et al., 2012);

Starch viscosity profile (Shen et al., 2007; Yuan et al., 2008; Lanning et al., 2012).

Rice cracking (Nagahata et al., 2004 and 2006);

Grain chalkiness (Morita 2005; Kondo et al., 2006; Nagata et al., 2006; Tsukaguchi et al.,

2008; Okada et al., 2009);

Head rice yield (Lanning et al., 2011).


Grain quality

o Quality is estimated as a function of the meteorological conditions experienced by the crop during the ripening period.

o Parameters with a clear biological meaning.

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Outline

Introduction


Application results

Conclusions



Field level

WARM model was compared with WOFOST and CropSyst, by evaluating theirperformances in reproducing data coming from field experiments carried outin Northern Italy (7 sites, 7 years).

Results indicate a comparable agreement for three models.

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Yieldforecasting

o The availability of operational monitoring systems is crucial to provide objective, timely and quantitative yield forecasts at regional and national scale;

o Most of the existing crop yield forecasting systems have been developed by coupling crop models with weather and soil databases in order to perform large-area simulations under potential and, in some cases, water limiting conditions.

o WARM model is used since years by the European Commission to forecast rice productions at EU 27 level;

o It was also applied to countries that play a major role in crop production at world level like China and India, and recently in Senegal, which is the 10th rice importer country, whose Government is pushing towards self-sufficiency.


Yieldforecasting

o To develop an effective crop yield forecasting system, three main points have to be considered: the need of the customer/user (European Commission, insurance

company); the spatial and temporal resolution of the forecast; the conditions of application (information available, agroclimatic

conditions)

26/09/200801/08/2008

o Rice yield forecasts at EU level consider the impact on crop production of spikelet sterility; blast disease

o Yield forecasts are updated during the growing season

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Yieldforecasting

o The use of the WARM model to crop yield forecasting in China and India closely followed the approach applied for Europe.

o Instead of measured meteorological data, ECMWF data were used. o WARM model was firstly calibrated using data coming from field

experiments, and then applied on a large scale on the Indian and Chinese rice cropped areas.


Yieldforecasting

o Senegal presents peculiar conditions for rice growth, very different from the European ones: Three main seasons: dry-hot, humid-hot and dry mild Low availability of advanced agromanagement practices

(fertilizations, herbicides) Two main growing regions, characterized by very different

agroclimatic conditionso Field level calibration

floweringmaturity

Date

AGB(t ha-1)

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Yieldforecasting

o The system includes the simulation of biotic and abiotic stresses; the assimilation of remotely sensed data

to determine sowing dates; as regressors to forecast yields.

o The elementary simulation unit is the 25 × 25 km cell of the grid weather (ECMWF data)

o Tests performed with multiple regressions with indicators from the first decade after maturity

o Different tests were performed, e.g., potential growth – model not calibrated – fixed sowing dates; biotic and abiotic limitation – model calibrated – fixed sowing dates biotic and abiotic limitation – model calibrated – variable sowing dates

R2 = 0.88 (R2 trend: 0.68)

Indicators:• Biomass• LAI• Storage organs• DVS

R2 = 0.96 (R2 trend: 0.68)

Indicators:• Blast infection events• Blast-LAI• Blast-storage organs• Brown spot infection events


Yieldforecasting

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Yieldforecasting

R2 = 0.96 (R2 trend: 0.68)

Indicators:• Biomass• LAI• Storage organs• DVS

R2 = 0.98 (R2 trend: 0.68)

Indicators:• Storage organs• DVS• LAI• Heat induced sterility


Continentalscale

The same modelling solution implemented in WARM 2 was used at continental scale to assess the impact of climate change on South America and Caribbean.

Simulations were performed by considering the implementation of adaptationstrategies (modification of the length of the crop cycle and shift in the sowing dates).

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Continentalscale

In 2020 the model forecasts an increase in rice potential production in Latin America even without adaptation strategiesThe projections are more encouraging if blast limited production is simulated

Rice –potentialproductionNo adaptationHadley A1B 2020

Rice –potentialproductionAdaptation Hadley A1B 2020

-15÷-5%

+5÷+15%+5÷+15%

-5÷+5%

-5÷+5%

Rice – blastlimitedproductionNo adaptationHadley A1B 2020

Rice – blastlimitedproductionAdaptationHadley A1B 2020

-5÷+5%

+5÷+15%

+5÷+15%

+15÷+25%


Continentalscale

The simulation of protein and amylose content suggest that a decided decrease in rice grain quality could be expected.

-15÷-5%

-25÷-15%

-5÷+5%

Rice – proteincontent (% difference to the baseline)Hadley A1B 2050

Rice – amylosecontent (% difference to the baseline)Hadley A1B 2050

-25÷-15%

-50÷-25%

-50÷-25%

25/10/2013

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In silicoideotyping

o Crop simulation models were developed aiming at two specific objectives:

Studying production enterprises, to maximize yields while optimizingthe use of resources, and

Studying genotype × environment interactions, as a tool to support variety selection and improvement. How is it possible?

2. Crop growth and development are simulated as a function of meteorologicaland agromanagementinputs.

1. Crop morpho-physiologicalfeatures are reproduced via model parameters.


In silicoideotyping

In Silico Ideotyping platform

ISIde1. Select the rice district 2. Develop the ideotype

3. Select weather scenario 4. View/export the results

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In silicoideotyping

o Yield increments given by the introduction of new ideotypes more resistant to blast disease are not homogeneously distributed neither in space nor in time.

o According to the combination GCM × weather scenarios, the results the magnitude of the obtainable benefits are different.

Hadley A1B

NCAR B1

Baseline 2020 2050 2085

8 16 units: %


Outline

Introduction


Application results

Conclusions


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Conclusions

o A software component able to simulate soil carbon and nitrogen dynamics in paddy rice fields is under development.

o This component will be linked to WARM 2 modelling solution and will allow to simulate the impacts of agro-management practices on crop growth.

o It will simulate the processes leading to gas emission: aerobic and anaerobic respiration; nitrification and denitrification; methanogenesis; ammonia volatilization.

o A requisite of this component is the balancebetween the detail adopted in the formalization of modelling approaches and the need in terms of inputavailability.


Conclusions

Thanks to the software architecture shared with BioMA, the WARM 2 modelling solution can be used to multiple purposes and spatial scales.

25/10/2013

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Conclusions

The user can configure its own WARM, thanks to the real time customization options present in the graphical user interface.


Outline

Introduction


Application results

Conclusions


25/10/2013

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Exercise

Explore WARM 2 GUI

Field level simulation (single year)

Flooded rice

Unflooded rice

Long term simulation

daily time step – actual weather

generation of synthetic future weather series(centred on 2020 and 2050)

hourly time step – future weather

Automatic calibration


Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice






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Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice







Floodedsimulation

o Load the configuration file “BASAL training workshop_fieldlevel.sim”

o Check the correctness of the simulation period

o Run the simulation

o View the outputs related to the different production levels

o View the graph of soil profile and the fate of pesticide

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Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice







Unfloodedsimulation

o Build a new agromanagement file from the graphical user interface: Sowing: fixed date 121 DOY Chemical treatment:

fixed date 140 DOY Tricyclazolo, amount 200 g ha-1, fraction 0.5)

Harvest: fixed date 320 DOY. Then click on

o Save and view it with the Agromanagement Configuration Generator

o Validate and run the simulation

o View the graph of soil profile and the fate of pesticide

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Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice







Daily time stepActual weather

o Load the configuration file “BASAL training workshop_longterm.sim”

o Uncheck all the options (we want to simulate only potential production level)

o Run the simulation

o View the outputs and save them (print screen)

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Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice



generation of synthetic future weatherseries (centred on 2020 and 2050)

Climate change simulations



Weathergeneration

o Go to the “Weather” page of the WARM 2 GUI

o Load the file “Historical weather series for generation.txt”

o Modify the monthly temperature deltas in rice growing period (May-September) 2020: +2°C 2050: +6°C

o Generate the weather series (CLIMAK weather generator)

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Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice







Climatechange

o 2020 weather, NO CO2 effect: daily time step simulation hourly time step simulation

o 2050 simulation, NO CO2 effect hourly time step simulation

o 2050 simulation, CO2 effect. Value 502 (average between A1B and B1 IPCC emission scenarios) hourly time step simulation

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DailyActual weather


Daily2020

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Hourly2020


Hourly2050

25/10/2013

36


Hourly2050 CO2


Exercise

Explore WARM 2 GUI


Flooded rice

Unflooded rice






10/25/2013

1

STICS – Principles of the model and implementation in Bioma

Remi Lecerf

125 October 2013

STICS

Stands for Simulateur mulTIdisciplinaire pour les Cultures Standard

(Multidisciplinary simulator of standard crops)

Developped by Agroclim Unit of INRA (Institut National de la

Recherche Agronomique)

http://www7.avignon.inra.fr/agroclim_stics

225 October 2013

http://www7.avignon.inra.fr/agroclim_stics

10/25/2013

2

STICS

Main characteristics of the crop model:

Based on the Radiation Use Efficiency concept

Simulates a wide range of variables related to the crop and the

soil

Simulates water and nitrogen balance

Various plants can be simulated: Wheat, sugar beet, vineyard,

sugar cane, grasslands…

Possibility of simulating crop management practices

Daily simulations

STICS is based on empirical or semi-empirical relationships and is

inspired from other models

325 October 2013

STICS

1.Principles of the model and main components

2.Main differences with WOFOST

3.Interface of the model and data needed

4.Implementation of STICS in Bioma

5.Results of the first simulations of grasslands

425 October 2013

10/25/2013

3

Main processes

525 October 2013

Water balance

Nitrogen balance

Transfer of heat, water, nitrogen in the soil

Crop management practices

Climate

Phenology

Shoot growth

Yield formation

Root growth

Phenological development stages

625 October 2013

Independent stages for shoot growth and harvested organs

Stages are defined in growing degree days

Phenological stages impacts shoot growth, radiation interception and biomass, root growth …

Photoperiod, vernalization and stresses if applicable slows down transition between development stages

10/25/2013

4

Development stages

725 October 2013

Determinate crops:Vegetative and reproductive growth occur successively

Wheat, maize, barley, grasslands

Indeterminate cropsVegetative and reproductive growth occur simultaneously

(at least partially)Tomato, vineyards, sugar beet, sugar cane

Development stages

825 October 2013

10/25/2013

5

Leaf dynamics

925 October 2013

The daily Leaf Area Index (LAI) is determined by:

A logistic function driven by a normalized development unit and where leaf growth rate depends on phenological stages

A thermal function where high temperatures are favorable to leaf growth until a plant dependent threshold

A density function only active when plant density reaches a threshold

The combination of all stresses (water, nitrogen, waterlogging)

DELTAI = DELTAIDEV . DELTAIT . DELTAIDENS . DELTAISTRESS

Radiation Use efficiency

1025 October 2013

Two methods are used, depending on the crop

Homogenous crops The radiation intercepted by the plant is a simple function that depends on LAI and an extinction coefficient specific to the crop

Row crops A simplified radiative transfer model is used taking into account spacing between rows, height of the plant…The model compute the direct and diffuse part of radiation

10/25/2013

6

Shoot Biomass

1125 October 2013

DLTAMS=

[EBMAX.RAINT −COEFB.RAINT2] . FTEMP(TCULT) . SWFAC . INNS . EXOBIOM . FCO2 + DLTAREMOBIL

Daily shoot biomass depends on:

Intercepted radiation (RAINT)

Suboptimal temperatures (FTEMP)

Water stress (SWFAC)

Nitrogen stress (INNS)

Impact of waterlogging on radiation interception (EXOBIOM)

CO2 effect on conversion use efficiency (FCO2 species dependent)

Carbon remobilization of winter reserves for perennial plants (DLTAREMOBIL)

Water stress

1225 October 2013

Two indices are computed, one is affecting leaf growth (TURFAC) and the other shoot biomass growth (SWFAC)

Water stress depends on:

Water content in the rooted zone

Stomatal functioning of plant (related to biomass growth) or critical potential of cell expansion (related to leaf growth)

Evaporative demand

10/25/2013

7

STICS/WOFOST : Phenology

STICS

Crop development stages are defined as a sum of growing

degree days (GDD)

Temperature is the main driver

Photoperiod and vernalization should be considered if needed

and slows down transition between stages

WOFOST

Development stages are driven by development rate

Development rate depends on temperature and a correction

factor which is crop dependent

1325 October 2013

STICS/WOFOST : Light utilization and biomassSTICS

RUE concept (Radiation Use Efficiency) Monteith (1972)

Simple descriptive method

Linear relationship between the light intercepted by the foliage

and the accumulated biomass A simple absorption coefficient

is used

WOFOST

Detailed description of gross photosynthesis and respiration

Model based on the CO2 assimilation

1425 October 2013

10/25/2013

8

STICS/WOFOST : Light utilization and biomassWOFOST

Aboveground biomass growth rate

Where

A = Gross assimilation rate (kg.CH2O.ha-1.day-1)

Rm = Maintenance respiration rate (kg.CH2O.ha-1.day-1)

Ce = Conversion efficiency off assimilates (kg.Dry matter.kg-1.CH2O)

1525 October 2013

STICS/WOFOST : Yields

STICS

Yields depends on a fixed harvest index, total aboveground

biomass, number of grains (depends on prevailing stresses

before the filling of harvested organs)

For grasslands, yields are corresponding to the total

aboveground biomass

WOFOST

Yields depends on partitioning during crop growth and total

aboveground biomass

1625 October 2013

10/25/2013

9

STICS/WOFOST : Root growth

STICS

Root growth depends on temperatures and eventually water

stress, waterlogging.

Root distribution is sigmoidal

WOFOST

Constant daily root growth defined by a daily root growth rate

Root distribution is linear

1725 October 2013

STICS/WOFOST : Stresses

STICS

Water deficiency (Depends on availability of water in the root

zone)

Nitrogen (Daily balance)

Waterlogging

Frost

WOFOST

Water deficiency (Depends on availability of water in the root

zone)

1825 October 2013

10/25/2013

10

STICS/WOFOST : Climate and evapotranspiration

STICS

Crop temperature is computed instead of air temperature

Input evapotranspiration or calculated:

- Penman

- Priestley-Taylor

- Shuttleworth and Wallace

WOFOST

Penman evapotranspiration

1925 October 2013

STICS/WOFOST : Partitioning

STICS

The method is used for harvested organs in some cases, determinate

plants and annual plants. The size of the envelopes of harvested

organs is computed as a function of shoot growth

No partitioning for indeterminate plants and perennial plants

WOFOST

Roots, stems, leaves and storage organs are partitioned among the

shoot growth

Partitioning factors are crop specific and depends on development

stages

2025 October 2013

10/25/2013

11

STICS/WOFOST

STICS

Crop management practices (sowing, harvesting, irrigation) may

be simulated

Soil surface: Impact of mulch, impact of tillage on bulk density

Possibility to force the model with external data (field

measurements, satellite data)

2125 October 2013

Input data of the STICS modelAs others model, 4 groups of input

2225 October 2013

- Rainfall

- Min and max temperature

- Radiation (Mj/m²/day)

-Wind speed (m/s)

- Vapor pressure (mbar)

- Evapotranspiration (mm)

- Sowing

- Mowing

- Irrigations

- Fertilization

- Soil depth (5 horizons)

- Soil water content (Field

capacity and water content

at wilting point)

- Bulk density

- Organic nitrogen in the

soil

- Stone content

- Clay and limestone of the

surface

- Maximum depth or roots

- Development stages

(GDD)

- Threshold

temperatures

- Parameters related

to LAI growth

-….

Climate (daily) Agromanagement Soil Plant

10/25/2013

12

STICS interface

2325 October 2013

STICS implementation in Bioma

Actually the model is coupled with EPIC to simulate soil evaporation,

water content

The implementation is only suitable for grasslands simulation

Some part of the model are missing:

simulation cannot be started before emergence

frost is not taken into account

nitrogen balance is not implemented

partitioning is not implemented

Automatic crop management practices are not implemented

2425 October 2013

10/25/2013

13

STICS implementation in Bioma

2525 October 2013

Grasslands simulations over France

Simulation of temporary grasslands only

No agromanagement practices are considered

Yields are assumed to be equal to the aboveground biomass

produced throughout the year

Main drivers of yields formation are considered to be soils and

climate (through water balance and photoperiod)

2625 October 2013

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Grasslands simulations over France

On the period 1990-2012

2725 October 2013

Grasslands simulation over France

The soil is simulated with EPIC

The location of temporary grasslands may differ from reality

(problematic if we consider that soils are driving yields)

Agromanagement practices are not taken into account

Species are not considered (rye grass is cultivated in the west of

France whereas the species simulated simulated are similar to

orchard grass/fescue)

The definition of temporary grasslands may

2825 October 2013

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Example of validation at local scale

2925 October 2013

Results of simulations for 48

plots (southeastern France)

Irrigated grasslands with 3

mowing

Agromanagement practices

were collected by the farmer and

used as input

Conclusion and perspectives

3025 October 2013

The model implementation is still under development:

- Implementation of soil simulation

- Implementation of development stages before emergence

- Implementation of Nitrogen balance

In order to estimate grasslands yields:

- Providing suitable agromanagement practices for grasslands

25/10/2013

1


Model calibration via the Optimizer

Davide Fanchini





Calibration

y = f(x, p)

p

x y

7642

Find a value for p that makes the model describe the real world

«as precisely as possible»

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2


Calibration: the Optimizer workflow

Selector

Outputunder

observation

Matcher

MatchedSeries

Model Caller

Parameters

Input Output

Objective FunctionSolver

Cost

Reference DataReference Data

Reader


Optimizer packages and macrocomponents

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3


The BioMA Optimizer

Can be run as a stand-alone application or as a BioMA plug-in

Calls the model using the same adapter of the BioMA application

(IModelCaller)

Comes with an extensible library of Solvers, Objective Functions

and Matchers

Relies on the Configuration layer, so these extensions can be

deployed, even by third parties, like the IModelCaller (but with

different adapters)


Optimizer: the application

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4





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5




Bristow&Campbell’s Solar Radiation Model

This model assumes a relationship between the range of daily

temperatures and the transmissivity of the atmosphere

TCSRADSRAD atmosphereoftopsurfaceearth Where TC is a transmissivity coefficient which varies from 0 to <1

averagemonthly

Tair

Tair

bCSTTC

2

exp1

Where CST is the clear sky transmissivity, and b an empirical

parameter

Problem: estimate the b parameter to minimize the difference

between predicted and measured values

One year of daily solar radiation data and air temperature data used

annex section - archived web sites - institute for environment...

Documents