Biometrics

Knowledge on individual characteristics

Use of an efficiency steering function

Modification of the individual ration

Increased efficiency

Water intake monitoring in pig and poultry farming

• Water meter is one of the simplest tools that farmers can use to monitor the performances of the animals

• Difference between expected and measured water intake • Very specialized information

• One device = one information • Few information for herd management

• Often captive systems

• Scalability? Compatibility? Interconnections? Lifespan?

• Data exchange with information system?

• A further risk to favor intensive systems ? ( P. Stevenson, ECPLF 2017)

Very attracting new technologies but…

• The biological knowledge and the technological possibilities will offer new perspectives and new indicators for PLF • Information management will play a decisive role

• Information system will be the heart of the device • Open and evolving IS to make the best use of information (IoT)

• The couple "information + robotisation" will offer innovative perspectives in research and husbandry • The importance of modelling to give value to the information • Consumers and society will benefit from the use of PLF as it will allow them to be informed on the welfare and health of the animals A future of PLF still largely to be built • Solutions need to be integrated into farm management systems

•Feeding

  single living organism lives/acts as the purely theoretical average • Time varying character -response to a stimuli or stressor might be different each time it happens • Dynamic : living organism evolves with aging • What can new technologies do? a precise definition of the target variable (ex : how to define lameness or mastitis?) A reliable reference measure of the target variable (ex : DMI from weighing system) Another variable in relation to the dynamics of the target variable and calculated/measured from field measurements at a high sampling frequency Define a reference point that indicates the point in time when the feature variable shows a deviation from the normal gait (careful analysis, understand the biological meaning) Detect the deviation from a healthy gait automatically, must enable calculation of the feature variable in real time Model the response to interpret the signal: ex progesterone (Friggens and Chagunda (2005) Development of PLF is strongly pulled by the dairy sector • Dairy cow stay longer in production, compared with any other farmed animals • Each animal unit is of high economical value • Individual management • Availability of milk sample/biomarkers • Increasing robotics (milking robots appeared 20 years ago) • PLF concept is rather new in the European pig industry • Early adopters start to use it • PLF is well developed in the poultry industry with a ration whose protein content is adjusted to maximize protein efficiency vs. feed in the entire herd with a balanced diet for the average animal

•

  Information that in itself is not always useful or usable • Too much information kills the decision! • Risk of inappropriate treatments with too early detection • Tools are still too focused on information (production of alerts) and not enough on the control of the systems • Still few tools to better use the potential of this information

••

  Sensors, data acquisition and treatment • Development of Bio/smart sensors • Dynamic multi-criteria approaches (MIMO), Self learning? • Precision buildings, source of new features (robotics, climate regul) Mechanistic whole animal models • Interpretation of data and signals from sensors • Integration of models into DSS that remain to be developed • More generic and interconnected Information systems • IoT to facilitate machine-processor communication (+ crowd sourcing, web) • Standardised exchange format • Make data retrievable, accessible, interoperable and re-usable • Innovative management tools for big data and cyber security Technical challenges Technical challenges • Integrate genomic information into precision breeding • Valuing the potentialities of big data for animal breeding • Development of animal phenotyping: integration of "omics" data and data from sensors • Access to new characters of interest • Digital tools must be adapted to the need of end-users (farmers) • Co innovations between researchers, farmers & stakeholders • Training farmers are essential to ensure clear benefits • Analyse the consequences on the farmer's job • Relationships with animals and with machines • Work organisation and new activities (maintenance of devices…) • Ergonomic of the insertion of the tools into the farm • Take into account geographic, socio-economic and farming systems variability across Europe • Need for Increase efficiency and/or collaborative digital tools • Consider the societal acceptance of these new technologies • Legal issue of intellectual properties • New business model for open data management and use Societal challenges Conditions of success: create value for farmers • The added value for farmers (advisory services, food chain) should be tested, validated and demonstrated in practice • The utility of many systems is not demonstrated • Need demonstration in farm, modelling, training • Development of appropriate tools for costs-benefits analysis

multi outputs systems, inter operability What's exciting about these new technologies is that they already meet needs that no one has yet

• Multi inputs -

Automated detection of repiratory infections by cough continuous monitoring


Fanny Calenge

Christophe Chassard

Aurélie Deveau

Moez Rhimi

Mahendra Mariadassou

Sandra Plancade

Sandrine Ellero

Julie Réveillaud

Samuel Mondy

Mark Morrison

Session 2) Microbial interactions and community response to disturbances

Keywords:

Preliminary catalogues of

Emmanuelle Maguin

  STLO, Rennes) & Francoise IRLINGER (GMPA, Grignon) Assembly of communities for the fermentation of cereals and protein crops 14:20-14:40 15 + 5 = 20 Olivier ZEMB (GenPhyse, Toulouse) PhageSoustract -Use of bacteriophages to perform knock-out in a bacterial community 14:40-15:00 15 + 5 = 20 Kim MILFERSTEDT (LBE, Narbonne) Enigme -Engineering interactions in methanogenic communities at the ecosystem level 15:00-15:20 15 + 5 = 20 Guillermina HERNANDEZ-RAQUET (LISBP, Toulouse) Integrating meta-omics data to elucidate the functionning of lignocellulolytic microbial consortia 15:20-15:30 10 3 flash posters presentations (3 min each): see titles page 4 P7-Stéphane CHAILLOU (MICALIS, Jouy) P14 -Hélène FALENTIN (STLO, Rennes) & Claire NÉDELLEC (MAIAGE, Jouy) P20 -Elisa MICHEL (SPO, Montpellier) [Salle plénière Louis Lumière] Marion LECLERC (MICALIS, Jouy), Pascale MOSONI (MEDIS, Clermont), Pascale SERROR (MICALIS, Jouy) 2) Modelling interactions for ecosystem understanding and steering [Salle Edison-Ampère] Thibault NIDELET (SPO, Montpellier), Simon LABARTHE (MAIAGE, Jouy) [Espace Meliès-Newton-Archimède] Speed-dating, animated by Christophe Chassard (UMRF, Aurillac), Stéphanie-Marie Deutsch et Hélène Falentin (STLO, Rennes) [Salles Edison-Ampère et Pascal-Descartes] of the presenting author(s)


.07

A (first) functional classification of permanent grasslands*

A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands Cruz et al., 2002. Fourrages Rich/fertile sites  What's a model?

A simplified and idealized representation of reality, based on an ordered set of assumptions relative to an observable and measurable phenomenon, and aiming to reproduce as well as possible the behaviour of the studied system.

 Modelling advantages compared with experimentation ?

• Allows to simulate complex ecosystems involving a lot of interactions and feedbacks and to address questions on the long-term (e.g. climate change)

•

••

  Photosynthesis

•••

  Considers a lot of influencing factors • It is easy to modify the model inputs, thus to control the simulation environment and to test a lot of scenarios The modelling of grasslands : an integrated view A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands (definition adapted from Coquillard and Hill, 1997) .017 A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands The modelling of grasslands : an integrated view  Different types of models • Stochastic (predictions have a random nature) or deterministic (a given input will always produce the same output) • Static (independent of time) or dynamic (generally use differential equations that are function of time) • Empirical (based on statistical equations that are just intended to be predictive) or mechanistic or process-based (equations are based on the understanding of the system functioning and thus are intended to explain processes); A mechanistic model always contains some empirical parts  What is the nature of existing grassland models ? • Are generally determistic, dynamic and mainly mechanistic / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands  The conception of a model is highly dependant of : • The objectives/the questions of the modeller • The knowledge about the system to model • The avaible experimental data to calibrate /validate the model  This greatly influences : • The spatial and temporal scales • The level of detail for the representation of processes • The modelling method Scales & level of detail of the model • Modelling method Adapted from Coquillard & Hill, 1997and Hirooka, 2010 Conception • Formalisation (equations) • Implementation (code) • Verification • Parameterisation/Calibration • Robustness and software reliability • Comparison of observed vs simulated values • Use of criteria of evaluation Evaluation Exploitation Responses to the questions adressed • Understanding of the simulated processes .020 A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands Based on the reading of this publication 1. What are the scientific and operational objectives (inputs, outputs)? 2. What is the modelled system (components and limits) ? 3. What are the time and spatial scales? 4. What are the assumptions ? The example of the ModVege model .021 A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands Why modelling grasslands ? The example of the ModVege model  Scientific objective • to simulate the dynamics of the biomass production, structure and forage quality in response to management and climate, in case of permanent pastures and temperate regions  Operational objective • to use this model in a whole farm simulator to represent each grassland field  has to be simple ! (not to model each species separately …)  outputs = inputs for an intake and production model composition = association (in constant %) of functional groups of species with similar functional traits (Cruz et al., 2002) : functional approach ! 2. Sward heterogeneity = the relative abundance of 4 structural plant components 3. Growth, senescence & abscission = continuous flows 4. Seasonal pattern of shoot growth = functional trait 5. Quality (digestibility) of green compartments, senescence and abscission are affected by compartment ageing 6. During harvest, 10% of the harvestable biomass is lost effects of variables on flows A.-I. Graux / Application of the functional analysis of soil-plantanimals interactions to the modelling of grasslands During the reproductive growth, growth is distributed between GV & GR = Reproductive function (REP) OMD of dead compartments is assumed constant  OMD of green compartments •  linearly with AGE from a maximum (at AGE=0) to a minimum (at maximum AGE) • The maximum AGE corresponds to: • leaf life span (LLS) for GV • duration of the reproductive period Empirical function (SEA)  Minimum (minSEA) in autum & winter (ST < 200°C d)   from the onset of growth (Residual biomass after cutting The pasture is considered to be cut 5 cm above ground level  Harvested biomass in each structural component hBM GV = BM GV -resBM GV and similarly for compartments GR, DV, DR  Total harvested biomass hBM = hBM GV + hBM GR + hBM DV + hBM DR

Growth (GRO) Senescence (SEN)

Calculation of the age

To go further …

.038

Merci de votre attention


Murielle Djiguemde

Dimitri Dubois

Mabel Tidball

Alexandre Sauquet

Dynamic games applied to common resources: modeling and experimentation -preliminary results

Motivation

Motivations :

Without regulation, Common Pool Resources (CPR) are subject to overexploitation (Hardin, 1968) To correctly anticipate the e ect of regulation, we need to understand how agents take decisions

Model

We study the behavior of two identical and symmetrical farmers, exploiting a renewable groundwater table in infinite time

The optimal control and the game the continuous time problem :

max w i (t) ∞ 0 e -rt     aw i (t) - b 2 w i (t) 2 Gross profit - Unitary cost (c 0 -c 1 H(t)) w i (t) Total cost     dt (1) st Ḣ (t) = R -αw i (t) :
max w i n ∞ n=0 (1 -rτ ) n β n aw i n - b 2 w 2 i n -(c 0 -c 1 H n ) w i n τ (2) st H n+1 = H n + τ (R -αw i n ) : the optimal control H n+1 = H n + τ (R -α w i n ) : the game H(0) = H 0 ,

  between discrete and continuous time, and the time horizon chosen for lab experiments What type of behavior will the experimental subjects exhibit : feedback, myopic, open-loop or social optimum ? Continuous time can be approched with discrete time ⇒ confront theory with Rubio & Casino (2003) ⇒ continuous time, infinite horizon Lab experiment : Janssen & al. (2010) ⇒ spatial aspect Theoretical with lab experiment : Herr & al. (1997) ⇒ discrete time, finite horizon Oprea & al. (2014) ⇒ compares continuous and discrete time Tasneem & al. (2017) ⇒ continuous time, infinite horizon

  the optimal control Ḣ (t) = R -α w i (t) : the game H(0) = H 0 , and H 0 given Model the discrete time problem :

  and H 0 given F -Feedback : groundwater table H(t) convergence for τ = the optimal control and 20 groups for the game Between subject design : di erent subjects for each treatment Calibration : a = 2.5; b = 1.8; α = 1; R = 0.56; c 0 = 2; c 1 = 0.1; r = 0.005; H 0 = 15 The dynamics of the resource : H t+1 = H t + 0.56w it : the optimal control H t+1 = H t + 0.56 -(w 1t + w 2t ) : the game F -Information on gross profit and unitary cost F -Comparing a player's behavior : optimal control and game MD, DD, MT and AS (: subjects were more myopic in the optimal control than in the game Econometric analysis not complete ⇒ correct time-series treatments Some extensions : Dicrete time lab experiment Experimentation : continuous time vs discrete time model Test the game without the optimal control Modify the given information MD, DD, MT and AS (ASFEE) Dynamic games and experimentation June 2018


domain: sdv shs

text:
Jean-Louis Fiorelli

Thomas Puech

Morgane Le Bris

Pasture Utilisation in a self-contained organic dairy grazing system

Half of the 'plot-years' stands a grass intake larger than 80% of their PU: they were just grazed but some were cut for refusals after grazing. The other plots were both grazed and cut for hay.

From year to year the plots nearly kept the same place inside the grazing management except in 2014 which was such a very special year that 16 out of 20 plots consist in a specific group.

  jean-louis.fiorelli@inra.fr An exclusively grass-fed dairy herd farmed from permanent pasture over experiment, Rotational grazing, 252 Grazing days per year (from Mr to No) 1,35 Cow / ha, 540 Kg DM hay / cow at grazing (6y /11) [vs 1482 Kg], 6070 Kg Milk / ha 0,87 LU / ha How to sum up the pasture utilisation experience issued from 11 years and 22 pasture ? Describe jointly spatial and temporal variabilities of grass utilisation (assessed by 'HerbValo model' -Delagarde et al., 2017) according to the specific plots features, climatic variations, grazing management and hay harvesting Multivariate analysis and clustering provide a synthesis of the 'plot-years' diversity: 9 groups of units to pool the plots with a same grazing function -Only grazed -cut for refusals if any--Early grazed, cut then grazed -Cut for hay then grazed


Cathleen Petit-Cailleux

Sylvie Muratorio

Francois Lefèvre

Hendrik Davi

Combining statistical and mechanistic models to unravel the causes of mortality causes within rear-edge beech population

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.