Towards a knowledge-based approach for an integrated control of early blight in potatoes (2013 – 2018)

Marc Goeminne1 Pieter Vanhaverbeke1

Kürt Demeulemeester2

Bernard De Baets3 Sofie Landschoot3

Jasper Carrette3

Michiel Vandecasteele3 Kris Audenaert3

Geert Haesaert3

early blight monitoring seasons 2013 - 2014 | some results exploring a tentative epidemiological disease model:

possibilities and difficulties

Weekly assessments + sampling Registration of all cultivation factors + weather data

Weekly assessments + sampling Registration of all cultivation factors + weather data

2013 very low disease incidence and severity levels (untreated fields: < 0,2% , 2nd half of September)

2014 low to medium disease severity levels (untreated fields: 0.1 to 5.0% by 1st week of September)

2014 low to medium disease severity levels (untreated fields: 0.1 to 5.0% by 1st week of September)

artificial inoculation trial 2014 cv. Bintje A. solani , 1 * 104 ml-1

date of inoculation

time

29 Jun 22:30

4 Jul. 23:45

25 Jul. 17:00

28 Jul. 16:00

6 Aug. 18:00

18 Aug.(*) 17:30

28 Aug. 16:00

2 Sep 18:30

4 Sep 16:00 (*) + plastic bag

“For a moment, nothing happened. Then, after a second or so,

nothing continued to happen.” Douglas Adams, The Hitchhiker's Guide to the Galaxy

artificial inoculation trial 2014 cv. Bintje A. solani , 1 * 104 ml-1

date of inoculation

time

29 Jun. 22:30

4 Jul. 23:45

25 Jul. 17:00

28 Jul. 16:00

6 Aug. 18:00

18 Aug. (*) 17:30

28 Aug. 16:00

2 Sep. 18:30

4 Sep. 16:00

observation date (**)

#lesions (***)

PD %disease

4 Aug. 0 10 0

6 Aug. 3 9.9 0.001

8 Aug. 2 9.9 0.001

12 Aug. 45 9.7 0.0035

14 Aug. 62 9.6 0.005

20 Aug. 2 /plant 9.1 0.5

22 Aug. > 50 /plant 9.0 1

27 Aug. - 6.5 19

2 Sep. - 6.0 25 (*) + plastic bag (**) 30/6 to 6/8 : 3 to 5 observations /week

(***) whole-field assessments

28/7/2014 16:10

28/7/2014 16:16

28/7/2014 16:17

no visible lesions between 30/7 and 5/8 first lesions observed on 6/8 (not-inoculated plant) epidemic start around 20/8

disease level on 22 Aug. lesions plant -1 average untreated field (0) 1 to 5 2.5 artificial inoculation 10 to 100 > 50

Clearly visible effect of all artificial inoculations, although no lesions observed between June 30 and Aug 5

First lesions (A. solani) on 6/8, independent from inoculations (untreated field, inoculated field & fungicide trial[untreated])

Artificial inoculations after 18/8 (same isolate, 1*104 ml-1) incubated after 90 to 120 hrs (3.8 to 5 days)

Leaf 1date time #lesions lesion area lesion growth avg /lesion #hours growth rate*hr-1

28/aug 16:00 0 0.0000 0.0000 0 0 01/sep 11:30 12 15.5038 15.5038 1.291979167 91.5 0.169442/sep 18:00 21 61.0338 45.5300 2.906369048 30.5 1.492793/sep 17:00 29 124.6188 63.5850 4.297198276 23 2.764574/sep 16:00 40 272.7875 148.1688 6.8196875 23 6.442129/sep 08:30 113 7500.0000 7227.2125 66.37168142 112.5 64.24189

Leaf 228/aug 16:00 0 0.0000 0.0000 0 0 0

1/sep 11:30 2 9.8125 9.8125 4.90625 91.5 0.107242/sep 18:00 13 21.9800 12.1675 1.690769231 30.5 0.398933/sep 17:00 17 39.8388 17.8588 2.343455882 23 0.776474/sep 16:00 18 74.7713 34.9325 4.153958333 23 1.518809/sep 08:30 92 2398.9600 2324.1888 26.07565217 112.5 20.65946

27/8/2014 14:28 28/8/2014 14:02

29/8/2014 16:25 2/9/2014 14:01

4/9/2014 13:17

from a disease level of approx. 2% to totally destroyed leaf

within 8 days

Both in 2013 and 2014, senescence of the crop and disease severity of A. solani seem to go ‘hand in hand’;

in the presence of A. solani, part of the senescence is

filled in by early blight lesions.

In 2013 and 2014, crop senescence (or tuber

ripening?) seems to be a condition

for development of early blight…

physiological resistance and growth stage of the crop seem to be at least equally (if not more?) important than weather conditions and presence of inoculum, in our climate.

senescence (or tuber ripening?) seems to be a condition for the (epidemic) development of early blight in a normally growing, non-stressed potato crop.

the spread of the disease (within a field, between fields) is much slower compared to late blight; nevertheless, leaf lesions can grow fast and destroy a healthy leaf within 8 to 10 days.

in a senescing crop, even severe attacks of early blight do not lead to significant yield (or quality) losses; other diseases at this stage play an equally (or even more?) important role in the observed non-significant yield reduction.

some tentative and preliminary conclusions on the early blight season 2014 (and 2013) …

“Towards a knowledge-based approach for an integrated control of early blight” aim is to develop a DSS for potato growers in Flanders what have existing models to offer for our region? (GDD, P-days, FAST, IWP, …)

conidiophore formation + sporulation

spread by wind and rain

germination

infection

lesion growth

spore potential

epidemiological disease model - possibilities and difficulties

disease cycle A. solani

• light | darkness • temperature • leaf wetness

• rainfall • LAI • wind speed • leaf wetness

• temperature • leaf wetness • RH

• leaf wetness • temperature • susceptibility of the plant!

• temperature • leaf wetness

(max. lesion size)

conidiophore formation + sporulation

spread by wind and rain

germination

infection

lesion growth

spore potential

Preliminary results for the hourly weather data in Kruishoutem 2014, without transferring the accumulated lesions to the next cycle (lesion size = 1 with every new cycle)

conidiophore formation + sporulation

spread by wind and rain

germination

infection

lesion growth

spore potential Next steps:

• adding spore potential and transferring accumulated lesions to next cycle

• adding physiological resistance of the crop by influencing the infection step

some pro ’s and con ’s + simulating with multiple runs can point out important

characteristics (of the fungus) and parameters (weather)

+ permits a focus on parameters and factors that are decisive and have the biggest impact on an epidemic

+ an approach that allows to track down ‘white spots’ in the knowledge

- how to assess and implement (and predict!) other factors that influence susceptibility?

- a polycyclic model with > 50 variables can probably simulate and prove almost anything, even climate change…

to be continued…

• adding wind speed measurements to the calculations!

• run the model for: o other regions (weather stations) o season 2013

• calculating lesion growth is cumbersome and time-consuming, hence some provisional programming imposes itself