Numerical technologies for agriculture 27/01/2015 Document confidentiel 1

Software Case study Services for all the partakers in agriculture

ICT View Increase agricultural production + 50% by 2050 Preserve natural resources Agriculture uses 70% of resources in water/year Preserve environment 100 millions tons of nitrogen spread each year Decrease GHG emission by agriculture (17 to 32%) Document confidentiel 3 Challenges Rise of digital agriculture Modeling and simulation tools Monitoring and data assimilation Decision aids tools Access to high performance computing

Services for every partakers in agriculture Document confidentiel 4 Variety breeding Crop management End-user transformation Yield forecast Ressources management Numerical technologies for agriculture

A generic modeling approach  Generic description of the interaction between the crop entities  Specific description of each process depending on the target : Research, comparative analysis and adaptation of formalisms from literature. I Confidential 5 Pool of biomass CHO H 2 O NO 3 - Allocation Seed Plant architecture Organ weights Phenological stages…

Compartmental approach: vegetative and reproductive compartments (e.g. STICS, LNAS) Specific mechanistic modeling  Several approaches for plant development Confidential 6 Architectural approach: organ-based (e.g. Greenlab) Biomass Cycle Biomass Cycle Biomass Cycle Cob Internode Leaf Biomass Foliage Biomass Stem Cycle organogenesis Biomass Cycle Yield

To take into account crop management, climate and soil properties Confidential 7 Soil-plant-atmosphere modeling Rainfall/irrigation Evaporation Infiltration Uptake Transpiration Interception Run-off Interception Manure, fertilizers, Crop residues NH 3 (g) N 2 (g) N 2 O (g) Mineralization Immobilization Uptake Leeching Allocation Radiation Transmission

Interaction with environment processed within plant growth model Parameters of the model stand for the specific genome of the plant Unfolding interactions between genotype and environment Environment of the plant Biomass allocation Biomass production Organs production Genome of the plant QTL Genome of the plant QTL Genetic model Physiological parameters: sources, sinks Mechanistic plant growth model Plant architecture Organ weights Phenological stages… Biomass allocation Biomass production Organ production Physiological parameters: sources, sinks Mechanistic plant growth model Confidential 8

Model calibration Confidential 9 Best fit Archival phenotype data (yield) to fit model parameters (~10) Thermal times for development stages Maximal harvest index Biomass accumulation rate Root growth rate … Physiological parameters Plant growth model Calibration from observation data Simulations Data

Phenotyping and experimental protocols  Numerical simulations for optimizing experimental protocols of in fields trials  Trade off between accuracy and experimental costs  Example : Accuracy on 3 parameters fitted with 2 data sets : Confidential 10 Minimal number of grains Maximal harvest index Thermal time lapse for vegetative growth 15 yield measurements + 10 LAI measurements

Use case architecture  Fitness function of a protocol  Genetic algorithm Document confidentiel 11 i-th parameter accuracy j-th observable cost drawn from sensitivity analysis P Environment classification Q protocol realisations of each classified env. Pool of indivduals PxQ Model parameters calibration

Optimizations on plant model work done with CINES experts B. Cirou, G. Hautreux  Memory management of plant state data  Profiling with Vtunes -> millions of memory allocation and erasing  Static buffer of states  50% speed-up  Approximations for exp() and pow()  ~ 9% speed-up  Call to last state in buffer buffer.last() replaced by temporary for each method  ~ 20% speed-up 12

13 Parallelization of application work done with CINES experts B. Cirou, G. Hautreux Master: Generation of P protocols P Environment classification Q protocol realisations of each classified env. PxQ Model parameters calibration Master: Genetic algorithm on protocols Master slaves I iterations

Scalability (1/2)  Model calibration 14 Master: Generation of P protocols P Environment classification Q protocol realisations of each classified env. PxQ Model parameters calibration Master slaves PxQ = 1x100

Scalability (2/2)  Model calibration 15 Master: Generation of P protocols P Environment classification Q protocol realisations of each classified env. PxQ Model parameters calibration Master slaves PxQ = 50x20

Conclusion  Production run with PxQ = 100x100  Curie for 400 kh for tests and production run  Scalable to 1024 cores  Parallelization and scalability could be improved  design  Hardware limitation with current design is CPU  Results show that 10 realizations per variety is enough  With detailed measurements on leaf area, biomass and stages.  Opposite to seed companies protocols >~ 100 realizations per variety!  Statistical modeling Bio-physical plant modeling Document confidentiel 16