Laboratory systems

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Increasingly, visual assessment of the onset of pathogen damage is supplemented by automatic sampling for fungal spores that can be identified subsequently in the laboratory. Monitoring the ingress of fungal spores identifies the early stages in the development of a disease epidemic and adds significantly to the precision with which decisions on the use of fungicidal sprays are made. Progress in establishing criteria for evaluating population expansion by leaf and pod spot (Alternaria species), light leaf spot (Pyrenopeziza brassicae), ringspot (M. brassicicola), powdery mildew (E. cruciferarum) and downy mildew (Peronospora parasitica) is substantial. Monitoring incoming airborne spores offers a direct measure of the risk of crop infection. For example, concentrations of the air-borne ascospores of S. sclerotiorum have been related

Table 7.1. Comparison of integrated pest control of diamond back moth (Plutella xylostella) with pheromone trapping and routine insecticide spraying on marketable yield of three Brassica crops.

Site 1 - Hebbal Site 2 - Devanahally

Treatment Cabbage Cauliflower Knol khol Cabbage Cauliflower Knol khol

Table 7.1. Comparison of integrated pest control of diamond back moth (Plutella xylostella) with pheromone trapping and routine insecticide spraying on marketable yield of three Brassica crops.

Treatment Cabbage Cauliflower Knol khol Cabbage Cauliflower Knol khol

Pheromone

19.3a

15.5a

17.5a

18.5a

14.0a

16.5a

(4 males/trap/night) Pheromone

16.5a

14.0a

16.2a

16.0a

12.5a

16.0a

(8 males/trap/night) Pheromone

14.5b

12.0a

15.4a

14.0b

10.0a

14.5a

(12 males/trap/night) Pheromone

8.0d

7.5c

16.5a

8.5d

7.0c

14.5a

(16 males/trap/night) Pheromone

4.9e

4.8d

9.2c

5.4e

3.9d

8.8c

(20 males/trap/night) Routine spraying 7 DAT

12.5c

8.5b

13.0b

10.8c

8.5b

12.4b

Routine spraying 9 DAT

13.0c

8.3b

12.7b

10.5c

9.0b

12.2b

Routine spraying 12 DAT

12.8c

8.7b

12.8b

10.7c

8.4b

12.4b

Routine spraying 15 DAT

12.5c

8.5b

12.5b

10.5c

8.6b

12.0b

Control - no pest control

3.6f

2.2e

4.2d

2.2f

1.8e

3.8d

The sites studied were in Karnataka State, India.

Marketable yield = t/ha.

DAT = days after transplanting.

Means within each column that are followed by a similar letter do not differ significantly when tested with Duncan's multiple range test, P = 0.05. After Reddy and Guerrero (2001).

to subsequent disease development in oilseed rape (B. napus) crops. Traditional methods of detection of air-borne inoculum are time-consuming, labour intensive and subjective, since they rely on the identification of spores by microscopy or aseptic cultural techniques. The ascospores of S. sclerotiorum, for example, are small, lack unique characteristics and are similar to those of other fungal species, and are therefore extremely difficult to identify morphologically with any degree of certainty.

Although the pathogen can be cultured, it is slow growing and it can take at least several days to obtain results. There is a need for new, more accurate and easy to use field-based methods. The ELISA (enzyme-linked immuno-sorbent assay) method is used successfully to detect virus pathogens especially in potatoes, and could be applied to pests and pathogens of Brassica vegetables. Recently, the potential of other molecular methods for the detection of air-borne microbes has been recognized. DNA-based methods are used to detect several species of air-borne bacteria. At present, DNA-based methods have only been developed for a few fungi. A PCR (polymerase chain reaction) assay for the detection of S. sclerotiorum involves specific primers designed to use nuclear ribosomal DNA (rDNA) internal transcribed spacer sequences (ITS). The ITS regions of nuclear rDNA evolve relatively rapidly and thus are highly variable, showing differences between closely related species and sometimes within species.

It is anticipated that molecular field test kits sensitive to unique genetic fragments of the pathogen will be marketed in the next few years. These will allow field detection of incoming fungal spores, leading to quicker prediction of disease development. The time lag between the arrival of spores and the expression of visual disease symptoms provided by early and rapid identification could increase the practical window during which control measures are applied. This increases very substantially the precision with which agrochemicals may be utilized, reducing their cost and potential environmental impact.

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