Sorghum in the 21st Century - development of effective disease … · 2018-06-20 · development of...
Transcript of Sorghum in the 21st Century - development of effective disease … · 2018-06-20 · development of...
+27-51-401 3666 [email protected] 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 | [email protected] | www.ufs.ac.za
Thank You
Dankie
THE SORGHUM TRUSTDO N
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