Construction and calibration of a nematode population dynamics models : Application to the management of plant parasitic nematodes in bananas

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CONSTRUCTION AND CALIBRATION OF A NEMATODE POPULATION

DYNAMICS MODEL

APPLICATION TO THE MANAGEMENT OF PLANT-PARASITIC NEMATODES IN BANANAS

Philippe Tixier

CIRAD, Banana & plantain research unit,

PRAM, BP 214, Le Lamentin, Martinique, FRANCE

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Why modelling population dynamics

of nematodes ?

·To formalize the nematologist knowledge

·To integrate multiple factors and processes

in interaction

·To catch the dynamical aspects

·To have a predictive tool (an in silico lab) ·To have a comprehensive approach of

complex systems

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What makes modelling plant-parasitic

nematode dynamics difficult ?

·The difficulties to evaluate

nematode population size:

·Aggregative distribution

·Not always reliable counting methods

·The need to integrate numerous

factors with their own dynamics

·The need to have a mechanistic and

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Objectives of a useful model

·To simulate at the field scale the evolution of

dynamics of plant-parasitic nematodes (in the

case of banana cropping systems : Radopholus

similis and Pratylenchus coffeae)

·To take into account : soil, climate, plant

characteristics, interaction among nematode species, different initial populations, effect of pesticide applications

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Modelling framework

·Based on a global cropping system

model (SIMBA,)

·Specifically developed for bananas

·Developed on the Stella HPS®

plateform

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SIMBA modelling framework

DECISION PROCESS GENERATOR

MULTICRITERION EVALUATION QUALITATIVE QUALITATIVE MODEL MODEL BIOPHYSICAL MODEL BIOPHYSICAL MODEL SOIL CLIMATE Pesticides risk Erosion risk Soil quality NEMATODES DYNAMICS SOIL COVER, STRUCTURE, NITROGEN… WATER BALANCE ECONOMIC RESULTS Profit margin AGRONOMIC PERFORMANCES Harvest dynamic ENVIRONMENTAL IMPACTS

Pesticides risk notes

Field scale Week step CROPS POPULATION GROWTH YIELD

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Model assumptions

Life-cycle of nematodes :

1. mature after 3 weeks

2. after 6 weeks nematodes die

3. Carrying capacity depends on root

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0 100 200 300 400 500 600 0 50 100 150 200 250

Young banana roots biomass (simulated)

weeks

Synchronized population Unsynchronized population

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Model assumptions

Life-cycle of nematodes :

1. mature after 3 weeks

2. after 6 weeks nematodes die

3. Carrying capacity depends on root

biomass (Quénéhervé 1989, 1993; Sarah 1986)

4. ≠ species compete for the root

resource (Duncan and Ferris, 1982; Shoener, 1983; Cadet and Debouzie, 1990; Umesh et al., 1994)

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Model assumptions

5. Population growth follows logistics

functions

(Ganry, personal communication; Hugon et

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Model assumptions

Logistic function 0 20 40 60 80 100 120 0 10 20 t N K c.N.(K-N) N dN dt = Carrying capacity

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Model assumptions

5. Population growth follows logistics

functions

(Ganry, personal communication; Hugon et

al., 1984; Quénéhervé, 1988; Quénéhervé, 1989)

6. Soil water content influence

growth rate

(Hugon et al., 1984; Mateille et al.,

1988; Quénéhervé, 1988; Quénéhervé, 1989; Vilardebo, 1984)

7. Quantity of nematicide in soil

influence the growth rate

(Cavelier, 1987;

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Model features

·Time step : 1 week

·Spatial scale : 1 plot ·Stock & flow model

·Inputs:

·climate,

·soil characteristics,

·Initial populations of each species

·Output:

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Schematic of SIMBA-NEM

1

Cohort

Rs 1 CohortRs 2 CohortRs 3 CohortRs 4 CohortRs 5 CohortRs 6

Rs mature Root Biomass Nematicide Soil water content Cohort

Pc 1 CohortPc 2 CohortPc 3 CohortPc 4 CohortPc 5 CohortPc 6

Pc mature C Pc K Pc

1

C Rs K Rs Rs₁ = (C_Rs * Rs_mature) * ((K_Rs – Rs_total) / K_Rs)

1 cohort represents of individuals of the same age (in week)

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Schematic of SIMBA-NEM

0 0,5 1 0 0,5 1 Environmental factor Co rr ec ti ve c oef fi ci en t Optimal value Maximal reduction Maximal reduction

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Model calibration and testing

·Calibration & validation realized

with a wide range of field

experiments realized in Guadeloupe and Martinique 0 100 200 300 400 500 600 0 10 20 30 40 50 60 70 Week of simulation N em at od e popu la tion (p er g ram o f f resh ro ot bi om as s)

crspot 0,4 crspot 0,6 crspot 0,8 crspot 1,0

·Sensitivity

analysis on parameters and inputs

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Some simulations

0 50 100 150 200 250 300 350 400 450 40 45 50 55 60 65 70 75 80

Weeks since planting

Ne ma tode population (per gram of fresh root biomass) 0 50 100 150 200 250 300 350 400 R o ot biom as s (gr a m.m -2 )

Rs Field data Rs Simulation Root biomass

Nematicide applications Nematicide applications

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Some simulations

0 100 200 300 400 500 600 20 40 60 80 100 120

Weeks since planting

N ematode population (p er g ram of f res h r oot biom as s) 0 90 180 270 360 R o ot biomass (gra m .m -2 )

Rs simulation Rs field data 11 Pc simulation Pc field data 11 Root biomass

Nematicide application

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What it permits to do ?

·Forecast when the populations

will growth

·Estimate the population level : nil,

low, medium, high

·Assess

ex-ante

the effect of the

previous crop in different environments

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How to use SIMBA-NEM to help the

field management ?

100 120 140 160 180 200 220 0 10 20 30 40 50 60 70 80 90 100

Week of nematicide application

M

ean nematode population _(per gr

am of

fr

esh ro

ot biomass)

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Future activities

·Implement different cultivars

and clones of banana Æ test scenarios

·Scaling down the model from the

field to the plant

·Integrate the contamination of

fields

·Takes into account the

interactions between nematodes and cover-crops…

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