The need to go beyond the pathogen in development of effective disease control strategies for sorghum. N.W. McLaren, M.C. Bester, L.A.

Transcription

1 The need to go beyond the pathogen in development of effective disease control strategies for sorghum N.W. McLaren, M.C. Bester, L.A. Rothmann University of the Free State PO Box 339 Bloemfontein 9330 South Africa

2 A widely accepted current concept: 1. pathogen colonizes a host 2. responds to the host environment 3. resulting in the manipulation of expression of its resistance genes (Lamichhane and Venturi) As a result: In the beginning specialist pathogens have become the major focus in plant pathology virulent on a narrow host range often limited to a single species or genus

3 In the beginning Most known plant genes for resistance to specialist pathogens confer qualitative resistance through innate immunity via large-effect loci that enable the recognition of the pathogen (Dangl and Jones 2001, Jones and Dangl 2006) Studies have demonstrated this type of host pathogen interaction in mono-species infections to the point of being race specific eg. wheat rusts

4 The Result: In the beginning Most plant pathology research is biased towards plant interactions with biotypes Associated with these are our concepts of co-evolution, PAMPS, gene for gene responses..

5 Where am I going with this? Populations of less specialized pathogens: have a mixed population providing a mixture of selectivity interactions with one another including antagonism synergism co-existence mutualism co-operation. The level of disease damage depends on these interactions and corresponding host responses

6 There is a range of population diversity: - within species - mixed-species communities E.g. Where am I going with this? 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

7 Generalist pathogens i.e. Where am I going with this? - virulent across a wide range of plant host species - have latent pathogenic capacities - cause disease when host conditions are suitable may become pathogenic should host physiological conditions change (Hentschel et al., 2000)

8 Where am I going with this? Pathogen Host Environment This is where agronomics becomes more important than genes Enter Epidemiology Systems analysis

9 Systems approach In an epidemic, the basic system is the interaction between host and pathogen (Kranz and Hau, 1980) This system's behaviour is defined by the environment Thus, there are numerous interlocking processes characterized by many reciprocal cause-effect pathways (Watt 1966) Pathogen The environment too consists of systems and Environment these systems interact and affect the behaviour of Host one another

10 Hierarchical Approach to Systems: In the systems approach the host and pathogen are themselves considered systems i.e. dynamic 1. Growth 2. Physiology 3. Anatomy 4. New organs 5. Canopy density Time + Space Host - Pathogen Disease 1. Survival 2. Proliferation 3. Structures sexual stages asexual stages conidia chlamydospores sclerotia 4. Races

11 Hierarchical Approach to Systems: Host - Pathogen Disease Weeds Insects Other diseases Biological constraint system Reservoir for pathogens Host stress - predisposition Vectors spread Host stress - predisposition Photosynthetic stress - predisposition 11

12 Hierarchical Approach to Systems: Weeds Biological Host - Pathogen Insects Control Other Disease diseases Chemical Biol constraint system control Pest management system 12

13 Host - Pathogen Disease Hierarchical Approach to Systems: Weeds Insects Other diseases Biological Control Chemical Biol constraint system control Pest management system Crop management system Variety Fertilizer Cultural practices Crop sequence Economy Information psychology 13

14 Hierarchical Approach to Systems: Host - Pathogen Disease Weeds Insects Other diseases Biol constraint system Pest management system Biological Control Chemical control Variety Topo Fertilizer climate Cultural practices Economy Crop sequence Society Economy Information Crop management system psychology Agroecosystem 14

15 The need to go beyond the pathogen!!!! 15

16 Relevance to sorghum pathology E.g.: Seedling blights and root rot of sorghum Locality Stand loss (%) Delmas Dover Heilbron Koster Koppies Standerton

17 Plant length (cm) Seedling blights and root rot of sorghum ERV=rv-(rr(%)*rv) Y = X R² = Effective root volume (ml) Cultivar Root rot severity (%) PAN8706W PAN PAN8648W PAN PAN PAN %

18 Pathogen complex Seedling blights and root rot of sorghum Macrophomina phaeseolina Fusarium moniliforme (sensu lato) Periconia circinata Pythium spp. Colletotrichum graminicola (Pande and Karunakar, 1992) Pyrenochaeta terrestris Rhizoctonia solani Sclerotium rolfsii Erwinia spp (Tarr, 1962) F. graminearum (sensu lato) F. oxysporum F. equisetti (Reed et al., 1983; Windels and Kommedahl, 1984) F. solani F. temperatum Alternaria spp. Phoma macrostoma P. sorghina Acremonium strictum Curvularia trifolii Colletotrichum capsici (Van Rooyen, 2012) Minor pathogens (Salt, 1979) opportunists; host-stress related

19 Seedling blights and root rot of sorghum Complex of soil-borne pathogens Destroy the root structure and volume o reduce water and nutrient uptake o reduce plant vigour o lodging Reduced grain yield (up to 25%) Reduced quality Distinct aerial symptoms not always visible

20 Incidence (%) Seedling blights: driving variables (temperature) Pre-em.damp.-off Post-em.damp.-off Minimum temperature ( C) 40 Damping-off (%) y = 3E+13x R² = Planting date (DOY)

21 Seedling blights: driving variables (Acid saturation; ph) Mesocotyl discoloration (%) Secondary root discoloration (%) Acid saturation (%) ph (KCl)

22 Root rot: driving variables (soils) Correlation coefficients - nutrient elements Root volume Root rot Yield N C Ca Mg K Fe Cu Zn Mn P (McLaren, 2004)

23 Root rot: driving variables (inherent root volume) Root rot severity (%) Root volume per plant (ml) Effective root volume (ml) 5 most susceptible PAN PAN PAN PAN PAN most resistant PAN PAN PAN8648W PAN8706W PAN LSD P<

24 Root rot: driving variables (rotations systems) Preceding crop NS5511 Root mass (g) Root rot rating (%) PAN 8706W Mean NS5511 PAN 8706W Mean Fallow ab c Monocult a b Dry Bean bc a Cow Pea bc a Soybean abc a Mean a b

25 Yield per plot (g) Root rot: driving variables (rotations systems) Preceding crop Effective root mass (g) NS 5511 PAN 8706W Mean Follow a Monoculture a Dry Bean bc Cow Pea c Soybean b 3000 y = e x Mean R² = Effective root mass (g) ( Van Rooyen, 2018) (McLaren, 2004)

26 Thus... Disease management lies with an integration of: Host characteristics root volume Soil condition ph; nutrients Temperature planting date Crop rotation nutrients; root volume Soil organic matter content biodiversity; suppression Stubble management survival; suppressive organisms Herbicide management chemical stress management Highlights the importance of epidemiology and systems analysis!! The need to go beyond the pathogen disease control strategies

27 Root rot: driving variables (GxE interactions)

28 Ergot of Sorghum A disease of unfertilized ovaries Incidence and severity pollen availability Pollen viability pre-flowering cold stress

29 Viable pollen (%) Ergot of Sorghum PAN8564 Y=69.7X-2.33X² R²=0.62 NK Y=1.33X R²= Mean minimum temperature ( C) (days pre-flowering)

30 Observed ergot severity (%) Y=8.71X 0.56 EBP=0.37 Ergot of Sorghum EBP 5% Y=0.18E-4X 4.62 EBP= Ergot potential (%)

31 Ergot incidence (%) PAN9 Y=0.521X R²=0.95 PAN31 Ergot of sorghum Y=0.273E-4X R²=0.99 PAN26 Y=0.015X R²=0.91 PAN16 Y=8.602X R²=0.89 PAN8 Y=1.732X R²=0.97 PAN54 Y=0.748E-5X R²= Ergot potential (%) PAN35 PAN12 Y=1.652X R²=0.97 Y=23.449X R²= Viable pollen (%)

32 Disease management lies in: Ergot incidence (%) Ergot of Sorghum Day of year avoiding pollen viability reducing conditions selecting for escape resistance in genotypes i.e. beyond the pathogen

33 Numerous fungi Diversity between species Interactions inhibition, co-habitation Co-incident with seasonal conditions Sorghum grain molds Alternaria alternata Colletotrichum graminicola Curvularia lunata Fusarium thapsinum F. semitectum Phoma sorghina (Singh and Bandyopadhyay, 2000)

34 Isolation frequency (%) Sorghum grain molds Cedara

35 FGSC chemotypes Sorghum grain molds ToxP1/ToxP2 Tri12 primers 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

36 Sorghum grain molds Tissue specific Deep seated e.g. F. graminearum (sensu lato) Superficial e.g. Phoma sorghina Alternaria alternata Colletotrichum graminicola Temp+Rh Locality flowering Greytown Y=( )+(( )- ( ))/(1+( )* EXP(-( )*(x))) R²=0.99

37 PC2 Sorghum grain molds 0.8 E A Model PC1 = 55% C. lunata PC2 = 32% B 0.6 Control F. graminearum F. thapsinum A. alternata P. sorghina Fungus -0.6 D Genotypes C -0.8 PC1

38 Sorghum grain molds Control flowering date management (similar to ergot) rotations biotic factors - insects Number of colony forming units NK283 Y=10.26X R 2 = 0.78 NK283 Y=15.21X R 2 = 0.91 Deltamethren Control PAN8706W Y=3.40X R 2 = 0.78 PAN8706W Y=16.93X R 2 = Number of puncture marks

39 The need to go beyond the pathogen in development of effective disease control strategies for sorghum Host - Pathogen Disease Biol constraint system Weeds Insects Other diseases Pest management system Crop management system Agroecosystem Biological Control Chemical control Variety Fertilizer Cultural practices Crop sequence Economy Information psychology Topo climate Economy Society 39

40 Systems analysis Allows for improvement of strategic and tactical decision making in crop protection based on statistical quantification of relationships between variables

41 Y = (-3.229*X 1 ) + Ergot Potential of a flowering date (pre-flowering cold stress) (days pre-flowering) EXP( *X 2 *X *X ) (daily maximum temp.) (days1-4 post-anthesis) + (0.379*X 3 ) Modelling and risk prediction (daily maximum RH) (days1-4 post-anthesis) Index of agreement d=0.94

42 Ergot incidence (%) Ergot Potential of a flowering date Feb Bethlehem (long term) Potchefstroom (long term) Day of year

43 Grain molds FgSC = MaxRH MaxT R 2 = 0.79 DON=0.06FgSC-0.035MaxT R²=0.84 NIV=3.68E-05FgSC+2.99E-03MinT R²=0.83 ZEA=7.12E-03FgSC+0.26MinT R²=0.92

44 Maize colonization by F. verticillioides

45 Fumonisin production by F. verticillioides

46 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 producers

47 The image part with relationship ID rid7 was not found in the file. DO T: +27(0) THE SORGHUM TRUST Thank You Dankie NOT COPY