+27-51-401 3666 mclarenN@ufs.ac.za www.ufs.ac.za

N.W. McLaren, M.C. Bester, L.A. Rothmann

University of the Free StatePO Box 339

Bloemfontein 9330South Africa

The need to go beyond the pathogen in development of effective disease control

strategies for sorghum

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A widely accepted current concept:

1. pathogen colonizes a host2. responds to the host environment3. resulting in the manipulation of expression of its

resistance genes (Lamichhane and Venturi)

As a result:

• specialist pathogens have become the major focusin plant pathology

• virulent on a narrow host range• often limited to a single species or genus

In the beginning

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Most known plant genes for resistance to specialistpathogens confer qualitative resistance through innateimmunity via large-effect loci that enable the recognition ofthe pathogen (Dangl and Jones 2001, Jones and Dangl 2006)

In the beginning

Studies have demonstrated this type of host–pathogen interaction in mono-species infections

to the point of being race specific eg. wheat rusts

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In the beginning

The Result:

• Most plant pathology research is biased towards plant interactions with biotypes

• Associated with these are our concepts of co-evolution, PAMPS, gene forgene responses …..

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Where am I going with this?

Populations of less specialized pathogens:

have a mixed population providing a mixture of selectivity

interactions with one another includingantagonism

synergism co-existence

mutualism co-operation…….

The level of disease damage depends on these interactions and corresponding host responsesDO N

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There is a range of population diversity:- within species - mixed-species communities

E.g.

Genome-wide association (GWA) mapping: B. cinerea- highly polygenic collection of genes- breeding would need to utilize a diversity

of isolates to capture all possible mechanisms

Where am I going with this?

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Generalist pathogens

- virulent across a wide range of plant host species- have latent pathogenic capacities- cause disease when host conditions are suitable

i.e.may become pathogenic should host physiological conditions change (Hentschel et al., 2000)

Where am I going with this?

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This is where agronomics becomes more important than genes

Where am I going with this?

Host

EnvironmentPathogen

Enter EpidemiologySystems analysisDO NOT C

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Systems approach

In an epidemic, the basic system is the interaction between host and pathogen (Kranz and Hau, 1980)

The environment too consists of systems

andthese systems interact and

affect the behaviour of one another

This system's behaviour is defined by the environment

Thus, there are numerous interlocking processes characterized by many reciprocal cause-effect pathways (Watt 1966)

Host

Pathogen

Environment

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Host - PathogenDisease

Hierarchical Approach to Systems:

In the systems approach the host and pathogen are themselves considered systems i.e. dynamic

1. Growth2. Physiology3. Anatomy4. New organs5. Canopy density

1. Survival2. Proliferation3. Structures sexual stages asexual stages

• conidia • chlamydospores• sclerotia

4. Races

Time +

Space

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Host - Pathogen

Disease

WeedsInsectsOther

diseasesBiological constraint system

Reservoir for pathogensHost stress - predisposition

Vectors – spreadHost stress - predisposition

Photosynthetic stress - predisposition

Hierarchical Approach to Systems:

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Host - Pathogen

Disease

WeedsInsectsOther

diseasesBiol constraint system

Pest management system

Biological Control

Chemical control

Hierarchical Approach to Systems:

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Host - Pathogen

Disease

WeedsInsectsOther

diseasesBiol constraint system

Biological Control

Chemical control

Pest management system

Crop management system

VarietyFertilizerCultural

practicesCrop

sequenceEconomy

Informationpsychology

Hierarchical Approach to Systems:

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Host - PathogenDisease

WeedsInsectsOther

diseases

Biol constraint system

Biological Control

Chemical control

Pest management system

Crop management system

VarietyFertilizerCultural

practicesCrop

sequenceEconomy

Informationpsychology

Topoclimate

Economy

Society

Agroecosystem

Hierarchical Approach to Systems:

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The need to go beyond

the pathogen!!!!

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E.g.: Seedling blights and root rot of sorghum

Relevance to sorghum pathology

DelmasDoverHeilbronKosterKoppiesStanderton

Locality Stand loss (%)

42.8053.7038.1028.9069.2058.30

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Seedling blights and root rot of sorghum

Cultivar Root rot severity (%)PAN8706W 29.09PAN8534 29.23PAN8648W 29.60PAN8229 45.42PAN8389 45.60PAN8568 47.00

Y = 58.674X0.1884

R² = 0.73

40

70

100

130

160

0 20 40 60

Plan

t len

gth

(cm

)

Effective root volume (ml)

ERV=rv-(rr(%)*rv)

25%

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Pathogen complex

Macrophomina phaeseolina Fusarium moniliforme (sensu lato)Periconia circinata Pythium spp. Colletotrichum graminicola (Pande and Karunakar, 1992)

Pyrenochaeta terrestris Sclerotium rolfsiiRhizoctonia solani Erwinia spp

(Tarr, 1962)

F. graminearum (sensu lato) F. oxysporumF. equisetti (Reed et al., 1983; Windels and Kommedahl, 1984)

F. solani F. temperatumAlternaria spp. Phoma macrostomaP. sorghina Acremonium strictumCurvularia trifolii Colletotrichum capsici

(Van Rooyen, 2012)

Seedling blights and root rot of sorghum

Minor pathogens (Salt, 1979) – opportunists; host-stress related DO NOT C

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• Complex of soil-borne pathogens

• Destroy the root structure and volumeo reduce water and nutrient uptake o reduce plant vigouro lodging

• Reduced grain yield (up to 25%)

• Reduced quality

Distinct aerial symptoms not always visible

Seedling blights and root rot of sorghum

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Seedling blights: driving variables (temperature)

10

20

30

40

50

11 12 13 14 15 16 17 18

Inci

denc

e (%

)

Minimum temperature (°C)

Pre-em.damp.-offPost-em.damp.-off

y = 3E+13x-4.94

R² = 0.510

10

20

30

40

200 250 300 350 400

Dam

ping

-off

(%)

Planting date (DOY)…DO NOT C

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Mes

ocot

yldi

scol

orat

ion

(%)

Seco

ndar

y ro

ot d

isco

lora

tion

(%)

Acid saturation (%) pH (KCl)

Seedling blights: driving variables (Acid saturation; pH)

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Root volume Root rot YieldN 0.04 -0.41 0.36C -0.11 -0.47 -0.02Ca 0.62 -0.61 0.36Mg 0.64 -0.81 0.37K 0.36 -0.64 0.23Fe -0.31 0.84 -0.38Cu -0.45 0.21 -0.57Zn 0.07 0.01 -0.13Mn 0.15 -0.31 -0.03P 0.81 -0.95 0.92

Correlation coefficients - nutrient elements

Root rot: driving variables (soils)

(McLaren, 2004)DO N

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Root rot severity (%)

Root volume per plant (ml)

Effective root volume (ml)

5 most susceptiblePAN8389 45.60 10.89 5.92PAN8229 45.42 13.44 7.34PAN8625 44.82 4.86 2.68PAN8568 43.80 21.67 12.18PAN8157 42.69 18.61 10.67

5 most resistantPAN8353 32.62 12.17 8.20PAN8556 31.18 15.78 10.86PAN8648W 29.53 17.40 12.26PAN8706W 29.42 7.13 5.03PAN8534 29.23 20.42 14.45

LSD P<0.05 5.77 5.21 2.83

Root rot: driving variables (inherent root volume)

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Preceding

crop

Root mass (g) Root rot rating (%)

NS5511PAN

8706WMean NS5511

PAN

8706WMean

Fallow 142.80 145.80 144.30ab 51.60 28.30 40.00 c

Monocult 95.60 112.00 103.80a 38.30 31.60 35.00b

Dry Bean 231.90 198.50 215.20 bc 43.30 18.30 30.83a

Cow Pea 313.10 182.90 248.00bc 41.60 16.60 29.17a

Soybean 283.30 180.10 231.70abc 36.60 25.00 30.83a

Mean 193.30 163.90 42.30a 24.00b

Root rot: driving variables (rotations systems)

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Preceding crop Effective root mass (g)

NS 5511 PAN 8706W MeanFollow 69.12 104.54 86.83a

Monoculture 58.99 76.61 67.80a

Dry Bean 131.49 162.17 146.83bc

Cow Pea 182.85 152.54 167.63c

Soybean 116.21 135.08 125.64b

Mean 111.73 126.19( Van Rooyen, 2018)

y = 707.99e0.0077x

R² = 0.57

0

1000

2000

3000

4000

0 50 100 150 200

Yiel

d pe

r plo

t (g)

Effective root mass (g)

Root rot: driving variables (rotations systems)

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Thus...Disease management lies with an integration of:

Host characteristics root volumeSoil condition pH; nutrientsTemperature planting dateCrop rotation nutrients; root volume

Soil organic matter content biodiversity; suppressionStubble management survival; suppressive organismsHerbicide management chemical stress management

Highlights the importance of epidemiology and systems analysis!!

The need to go beyond the pathogen – disease control strategiesDO NOT C

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Root rot: driving variables (GxE interactions)

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Ergot of Sorghum

• A disease of unfertilized ovaries

• Incidence and severity ∝ pollen availability

• Pollen viability ∝ pre-flowering cold stress

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50

60

70

80

90

100

11 13 15 17

PAN8564

Y=69.7X-2.33X²-424.78R²=0.62

NK283

Y=1.33XR²=0.83

1.494

Mean minimum temperature (°C)(days 23-27 pre-flowering)

Viab

le p

olle

n (%

)

1.494

Ergot of Sorghum

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0

10

20

30

40

50

0 5 10 15 20 25

Y=8.71X 0.56

EBP=0.37

Y=0.18E-4X 4.62

EBP=15.11

EBP 5%

Ergot potential (%)

Obs

erve

d er

got s

ever

ity (%

)Ergot of Sorghum

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20

40

60

80

100

0

20

40

60

80

0 20 40 60 80 20 40 60 80 100

20

40

60

80

20

40

60

80

100

20

40

60

80

20

40

60

80

20

40

60

80

20

40

60

80

0

PAN9

Y=0.521X0.862

R²=0.95

PAN8

Y=1.732X0.970

R²=0.97

PAN26

Y=0.015X2.081

R²=0.91

PAN35

Y=1.652X0.895

R²=0.97

PAN31

Y=0.273E-4X3.323

R²=0.99

PAN54

Y=0.748E-5X3.935

R²=0.99

PAN16

Y=8.602X0.545

R²=0.89 PAN12

Y=23.449X0.274

R²=0.89

Ergot potential (%)

Viable pollen (%)Er

got i

ncid

ence

(%)

Ergot of sorghum

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Disease management lies in:

avoiding pollen viability reducing conditions

selecting for escape resistance in genotypes

i.e. beyond the pathogen

0

10

20

30

40

50

25 45 65 85 105

Ergo

t inc

iden

ce (%

)

Day of year

Ergot of Sorghum

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Sorghum grain molds

Numerous fungi

• Diversity between species

• Interactions – inhibition, co-habitation

• Co-incident with seasonal conditions

Alternaria alternataColletotrichum graminicolaCurvularia lunataFusarium thapsinumF. semitectumPhoma sorghina

(Singh and Bandyopadhyay, 2000)

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0.00

5.00

10.00

15.00

20.00

25.00

Isol

atio

n fr

eque

ncy

(%) Cedara 2009-2012

Sorghum grain molds

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Sorghum grain molds

ToxP1/ToxP2 primers Tri12 primers

Species Locality Crop DON NIV 3-ADN 15-ADN NIV

F. boothii W’fontein Maize + - - + -F. boothii F‘fort Maize + - - + -F. boothii B’ lehem Maize + - - + -F. meridionale Cedara Sorghum - + - - +F. meridionale P’stroom Sorghum - + - - +F. cortaderiae Cedara Sorghum - + - - +F. acaciae-mearnsii Cedara Sorghum - + - - +F. meridionale P’stroom Sorghum - + - - +

FGSC chemotypes

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Sorghum grain molds

GreytownY=(510.606251513941)+((282.313429813325)-(510.606251513941))/(1+(347977.182760671)*EXP(-(.38871573656899)*(x))) R²=0.99

• Tissue specific

• Superficial e.g. Phoma sorghinaAlternaria alternataColletotrichum graminicola

• Deep seated e.g.F. graminearum (sensu lato)

Temp+RhLocality flowering

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A. alternata

C. lunata

F. graminearum F. thapsinum

P. sorghina

Control

1

2

3

4

5

6

7

8

9

10

11

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Fungus

Genotypes

A

C

D

EModel PC1 = 55% PC2 = 32% B

PC1

PC2

Sorghum grain molds

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Sorghum grain molds

Control

flowering date management

(similar to ergot)

rotations

biotic factors - insects

0

40

80

120

0 2 4 6 2 4 6

40

80

120

NK283Y=15.21X+ 21.64R2 = 0.91

Deltamethren

Control

NK283Y=10.26X+ 7.19R2 = 0.78

PAN8706WY=3.40X+ 11.33R2 = 0.78

PAN8706WY=16.93X+ 16.25R2 = 0.78

Number of puncture marks

Num

ber o

f col

ony

form

ing

units

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Host - PathogenDisease

WeedsInsectsOther

diseases

Biol constraint system

Biological Control

Chemical control

Pest management system

Crop management system

VarietyFertilizerCultural

practicesCrop

sequenceEconomy

Informationpsychology

Topoclimate

Economy

Society

Agroecosystem

The need to go beyond the pathogen in development of effective disease control strategies for sorghum

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Systems analysis

• Allows for improvement of strategic and tactical decision making in crop protection

based on statistical quantification of relationships between variables

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Ergot Potential of a flowering date

Y = (-3.229*X1 ) (pre-flowering cold stress) (days 23-27 pre-flowering)

+

EXP(-0.0029*X2*X2 +0.0936*X2 +3.061) (daily maximum temp.) (days1-4 post-anthesis)

+

(0.379*X3) (daily maximum RH)(days1-4 post-anthesis)

Index of agreement d=0.94

Modelling and risk prediction

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0

10

20

30

40

50

60

0 50 100

Ergo

t inc

iden

ce (%

)

Day of year

Ergot Potential of a flowering date

Bethlehem (long term)

Potchefstroom (long term)

1 Feb

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Grain molds

FgSC = 216.86MaxRH82-95 – 746.75MaxT 82-95 R2= 0.79

DON=0.06FgSC-0.035MaxT101-115 R²=0.84

NIV=3.68E-05FgSC+2.99E-03MinT91-104 R²=0.83

ZEA=7.12E-03FgSC+0.26MinT100-113 R²=0.92

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Maize colonization by F. verticillioides

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Fumonisin production by F. verticillioides

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Going beyond the pathogen

Allows for improvement of strategic and tactical decision making in crop protection

• based on statistical quantification of relationships between variables

• describes system behavior (quantitative)

• the current state of the system can be used to make projections

• improved epidemiological knowledge – improved decision making for producersDO N

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T: +27(0)51 401 9111 | info@ufs.ac.za | www.ufs.ac.za

Thank You

Dankie

THE SORGHUM TRUSTDO N

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