XV AIAM Congress, Palermo, June 5 - 7, 2012 CROP MONITORING IN EUROPE MARS-AGRI4CAST activities on...

XV AIAM Congress, Palermo, June 5 - 7, 2012

CROP MONITORING IN EUROPE

MARS-AGRI4CAST activities on crop monitoring, yield forecasting and climate change

Giovanna FontanaOn behalf of the MARS –AGRI4CAST team

IES - Institute for Environment and SustainabilityIspra - Italy

http://mars.jrc.ec.europa.eu/http://ies.jrc.ec.europa.eu/

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Outline

● The JRC MARS Unit

● Scenario analysis in agriculture

● The modelling system

● The BioMA platform

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The IES MARS Unit mission…

Focusing on crop production and agricultural activities, the MARS Unit provides timely forecasts, early assessments and the scientific underpinning for efficient monitoring and control systems.

The work serves the Agriculture and Food policies of the European Union, their impact on rural economies and on the environment, encompassing the global issues of food security and climate change.

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MARS (Monitoring Agricultural ResourceS) Unit:

AGRI4CAST: crop production forecasts initially in EU member states, then in neighboring countries; scenario analysis for current and future climate

AGRI-ENV: Integration of Environment concerns into Agriculture

FoodSec (Food Security Assessment): crop monitoring & early warning for DG DEVCO mainly in sub-Saharan Africa

GeoCAP: Geomatics for the Common Agricultural Policy control


The IES MARS-AGRI4CAST Action runs:• The Crop Growth Monitoring System (CGMS), providing in season

production estimates to DG AGRI;

• Scenario analysis of climate change impact on agriculture, providing software tools, inclusive of data and models;

MARS-AGRI4CAST activities have led to the development of:

• Several weather database covering Europe, and areas in Latin America, Asia, and Africa;

• A modelling platform, BioMA (Biophysical Models Applications), which allows running an extensible set of modelling solutions against a spatially explicit database.


Outline





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Scenario analysis in agriculture

MARS uses data on weather, soils, crops, and agricultural management to make impact assessment and to estimate the outcome of adaptation strategies.

Times series of weather data are used as driving force with process based biophysical models to simulate the dynamics of the system.

The analysis can integrate several aspects of a production system to simulate its performance (e.g. yields and water use of a cropping system), or may target specific sub-systems (e.g. potential pressure of a diseases on a crop).

In the following slides examples of both procedures and results taken from analysis carried out are shown, as well as basic elements of the modelling and software systems.

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Weather dataFrom climate scenario data to model inputs

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Weather data workflowGrid of 25 x 25 km


Scenarios of future climate are extremely variable even within the same hypothesis of green house gases emission.


Climate change impacts in LAC

Datasets – Climate (GCM: HadleyUK MetOffice; NCAR)

ECMWF ERA-Interim reanalysis (1989 – present, 25×25 km spatial resolution);

IPCC AR4 emission scenarios A1B and B1;

Hadley3 and NCAR CGMs;

2020 – 2050 time frames.

CLIMAK weather generator coupled to CLIMA libraries

Model simulation workflowsImpact assessment and definition of adaptation strategies

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Simulating impact and adaptation


Wheat – yield gap (water availability)


Wheat – water limited, no adaptation


Wheat – best adaptation (%)


Wheat – best adaptation (t ha-1)


Wheat – adaptation technique


Quality: Rice Amylose/Amilopectin ratio


Quality: Rice grains chalkiness


Soil-Borne plant diseases

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Outline





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The modelling system

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Plant libraries

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Many crop models are available in the literature, sharing part of the approaches and differing for others.

There is not such a thing as the universal “best model”; instead, under specific conditions of applications and according to the specific objective of the modelling study, different tools can be adequate.

If modelling knowledge is shared in software libraries including different approaches, it becomes possible to compare models and to further develop them by adding either new or missing approaches.


The CropML and CropML-WL libraries

CropML and CropML_WL are two libraries of models for crop growth and development under potential and water limiting conditions.

They currently implement modelling solutions with different approaches for biomass accumulation, photosynthates allocation to plant organs and leaf area evolution

● CropSyst (generic crop / grasses simulator)

● Wofost (generic crop simulator)

● WARM (rice)

● STICS (being implemented - generic crop / grasses simulator)

● CaneGro (being implemented - specific for sugarcane)

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Air-borne plant diseases simulation

● Despite of their key role in determining actual production levels, the impact of plant diseases on the year-to-year yield fluctuations is completely ignored in crop yield simulation systems.

● Forecasting frameworks have been developed for a number of plant diseases, but the coupling of disease forecasting models to crop growth models is not yet operational.

● it is crucial to deal with the implementation of models for the simulation of the dynamics of plant diseases and of the plant-pathogen interactions aiming at quantifying biotic yield losses.


The Diseases components are software libraries consisting of four components which provide a generic frame to simulate disease development: DiseaseProgress, InoculumPressure, ImpactsOnPlants, AgromanagementImpact.

Each component is developed as a generic model unit to simulate various aspects of a polycyclic epidemic caused by fungal pathogens.


● The framework is targeted at simulating a generic fungal polycyclic epidemic considering the impact of weather variables, agricultural management and host characteristics (resistance, phenology, growth);

● The development of the Diseases components followed the guidelines drawn by Donatelli and Rizzoli (2008):

Independent software unit

Extensible by third parties

Fine granularity of the modelling approaches implemented

Bridge, Strategy, Composite, and Context Design patterns

Inputs, outputs and the parameters in Domain classes

Unit tests, model and code documentation available.


Outline





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The BioMA framework● Analyzing agriculture and climate change implies targeting multiple

objectives, in a variety of environmental and agro-management contexts;

● New relevant data layers are becoming available and are planned for the near future;

● Impact of GHG emissions on climate must be studied with global models, but impact on production systems must be analyzed at local level;

● There is also a clear demand for integration with other domains, to add economic, social and environmental dimension to the analysis;

These points directly define requirements for the tools needed, which have been leading to the design of a flexible modeling platform open for collaborative development.


What is BioMA?● BioMA (Biophysical Models Applications) is a software framework

designed and developed for analyzing, parameterizing and running modeling solutions based on biophysical models against database which include spatially explicit units;

● The framework is based on framework-independent components, both for the modeling solutions and the graphical user's interface;

● The component-based structure allows BioMA to implement diverse modeling solutions targeted to specific modeling goals, allowing also for adding new modeling solutions independently by third parties;

● The goal of this framework is to rapidly bridge from prototypes to operational applications, enabling also running and comparing different modeling solutions.


BioMA features and peculiarities

The guidelines followed during its development aimed at maximizing:•Extensibility with new modeling solutions•Transparency of the modeling domain •Ease of customization in new environments•Ease of deployment •Possibility for extension independently by third parties

The reuse of existing models, besides their re-implementation to ensure the functionalities of the platform, was also chosen as a base for making available modeling options.


BioMA current modelling solutions

The current version of BioMA includes “heterogeneous” modelling solutions:

● WARM (Rice simulation)● CropSyst-Water Limited (Generic crop/cropping systems

simulator)● WOFOST-Water Limited (Generic crop simulator)● APES (Cropping systems simulator)● DSSAT- Canegro (Sugarcane – being developed)● PotentialDiseaseInfection (Airborne plant diseases)● PotentialSoilDiseaseIfection (Soilborne plant diseases)● Diseases (Plant diseases linked to crops)● GrainQuality (Currently rice)● ClimIndices (Climatic indices)

All model components can be extended (e.g. the new WOFOST).


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BioMA structureThe software architectural layers

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BioMA: from models to viewersModel layer: fine grained/composite models 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


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Production levelsModelling solutions in BioMA allow estimating different production

levels, increasingly integrating yield limiting factors:

● Potential production (P: crop growth solar radiation and temperature driven)

● Water limited (WL: all factors of P and water limitation)● Abiotic stress limited (AL: P and effects due to temperature

stresses of extreme events for crops)● Disease limited (DL: P and impact from one crop-specific

disease)● Multiple-factor limited (MFL: P, WL, AL, and DL limited)

P set limits which can be potentially overcome by genetic improvement/choice of species, WL/DL set limits which can be overcome via agricultural management


BioMA features and peculiarities

BioMA is provided with supporting tools for developers and users:

● LUISA: Monte Carlo based sensitivity analysis, implementing 7 sensitivity analysis methods;

● Optimizer: Automatic calibration extensible for objective functions and solvers;

● IMMA: Model evaluation, based on simple and composite metrics for quantifying models performances and complexity.

These tools are extensible and re-usable also outside BioMA.

BioMA graphical user interfacesData visualization

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XV AIAM Congress, Palermo, June 5 - 7, 2012cl

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Variables of time series are selected by the user


BioMA documentation


BioMA and components availability


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Conclusions – The BioMA platform

Applications developed on the base of the BioMA framework are currently capable to address many aspects related to the biophysics of agricultural production;

BioMA neither is a model nor proposes a model; instead, it is an open platform to make available in operational software the results of research on biophysical modeling in agriculture;

Adopting a component oriented development, extended both to models and tools, fosters reusability without forcing third parties toward investing exclusively on a specific framework they do not own;

We make available BioMA as a platform, but also, and of no lesser importance, as a loose collection of model objects and software tools reusable in other platforms.


Muchas gracias por su atención…

BioMA Scientific LeaderMarcello Donatelli

Biophysical ModellersMarcello Donatelli, Roberto Confalonieri, Simone Bregaglio, Giovanni Cappelli, Caterina Francone, Amit Srivastava, Marco Acutis, Francesco Tubiello

Software EngineersIacopo Cerrani, Davide Fanchini, Davide Fumagalli, Andrea Rizzoli, Antonio Zucchini

Credits

… and all the scientist that have developed in the last decades of research many of the models made available as base modeling reference in the platform!

XV AIAM Congress, Palermo, June 5 - 7, 2012 CROP MONITORING IN EUROPE MARS-AGRI4CAST activities on...

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