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SID 5 Research Project Final Report

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NoteIn line with the Freedom of Information Act 2000, Defra aims to place the results of its completed research projects in the public domain wherever possible. The SID 5 (Research Project Final Report) is designed to capture the information on the results and outputs of Defra-funded research in a format that is easily publishable through the Defra website. A SID 5 must be completed for all projects.

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Project identification

1. Defra Project code HH3215TFV

2. Project title

Forecasting Sclerotinia disease in field grown lettuce

3. Contractororganisation(s)

Warwick HRIUniversity of WarwickWellesbourneWarwick

54. Total Defra project costs £ 441,033

5. Project: start date................ 01 January 2003

end date................. 31 December 2005

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6. It is Defra’s intention to publish this form. Please confirm your agreement to do so...................................................................................YES NO (a) When preparing SID 5s contractors should bear in mind that Defra intends that they be made public. They

should be written in a clear and concise manner and represent a full account of the research project which someone not closely associated with the project can follow.Defra recognises that in a small minority of cases there may be information, such as intellectual property or commercially confidential data, used in or generated by the research project, which should not be disclosed. In these cases, such information should be detailed in a separate annex (not to be published) so that the SID 5 can be placed in the public domain. Where it is impossible to complete the Final Report without including references to any sensitive or confidential data, the information should be included and section (b) completed. NB: only in exceptional circumstances will Defra expect contractors to give a "No" answer.In all cases, reasons for withholding information must be fully in line with exemptions under the Environmental Information Regulations or the Freedom of Information Act 2000.

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Executive Summary7. The executive summary must not exceed 2 sides in total of A4 and should be understandable to the

intelligent non-scientist. It should cover the main objectives, methods and findings of the research, together with any other significant events and options for new work.

Sclerotinia sclerotiorum is a major soilborne fungal pathogen causing Sclerotinia disease on many important crops both in the UK and world-wide. In the UK, the disease is a major limiting factor in growing field lettuce and a loss of just 15% of the crop would currently cost the industry £12 M. Sclerotinia control relies on the use of fungicides but efficacy of control is variable because it is difficult to determine when disease risk is high and hence when to apply the limited number of sprays that is allowed. Good timing of fungicides is essential not only for effective disease control but also to avoid unnecessary or prophylactic applications which are expensive, environmentally undesirable and could potentially lead to fungicide resistance developing in Sclerotinia. The aim of this project, therefore, was to develop a forecasting model for Sclerotinia disease based on an understanding of the environmental factors affecting key stages in the pathogen’s life cycle so that fungicide sprays can be targeted to periods when disease risk is high. This therefore addresses Defra policy for promoting a modern, adaptable, diverse and sustainable farming industry as well as a competitive and safe food supply chain which is responsive to the needs of consumers.

The project has focussed on developing mathematical models which can use weather data to predict 1) when S. sclerotiorum sclerotia (resting bodies) in the soil germinate to produce mushroom-like apothecia and 2) when airborne ascospores released from these apothecia infect lettuce plants and disease symptoms develop. In order the achieve this, experiments were carried out under controlled conditions so Soil temperature and moisture content (measured as water potential) were the major factors affecting germination of S. sclerotiorum sclerotia to produce apothecia. Furthermore it was found that a cold ‘conditioning’ period was required by the sclerotia to allow rapid germination. Models for conditioning and subsequent germination were derived for two isolates of S. sclerotiorum which showed that conditioning rate was fastest at 4-5°C and germination rate at 18-20°C. Soil moisture above a certain threshold level was also required for germination to occur. Field experiments were also conducted at two field sites where sclerotia were buried at regular intervals throughout the year and the appearance of apothecia monitored. Typically it was found that the sclerotia buried between December and April germinated well but few or no apothecia were produced for sclerotia buried from mid-May to September in the same year. Using weather data collected from these field sites, the germination models were able to successfully simulate this pattern of germination but the accuracy of the actual time of germination was variable.

Sclerotinia disease occurred for a wide range of RH levels in experiments and was fastest at 15-25°C. Although ascospores only germinate if RH > 97% in the laboratory, infection and disease still occurred on inoculated lettuce plants maintained at RHs as low as 50%, but disease progress was much slower than at higher RH. To explain this observation, it was concluded that under low ambient RH conditions, there are

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microsites at the stembase of the lettuce plant where RH is still high enough to support infection. A Sclerotinia infection model was therefore developed which included the concept of an active infection court area (ICA) representing these microsites. When ambient RH levels are high, the model ICA increases in size allowing more S. sclerotiorum spores to germinate, hence promoting faster disease development. Conversely, when ambient RH levels are low, the ICA is small and fewer spores germinate. Other important factors affecting Sclerotinia infection and the development of disease symptoms were also incorporated into the model including lettuce size, spore concentration and the effect of temperature. Validation of the Sclerotinia infection model in the field showed that it could predict levels of Sclerotinia when the observed incidence was > 5-10% but that on some occasions when the disease level was lower or zero, it was less accurate.

The models for germination of sclerotia and disease development of lettuce were combined at the end of the project to produce a preliminary forecasting model for Sclerotinia disease with the potential to predict when Sclerotinia disease occurs in lettuce so that fungicides can be deployed appropriately. The system now requires testing in commercial lettuce crops.

Project Report to Defra8. As a guide this report should be no longer than 20 sides of A4. This report is to provide Defra with

details of the outputs of the research project for internal purposes; to meet the terms of the contract; and to allow Defra to publish details of the outputs to meet Environmental Information Regulation or Freedom of Information obligations. This short report to Defra does not preclude contractors from also seeking to publish a full, formal scientific report/paper in an appropriate scientific or other journal/publication. Indeed, Defra actively encourages such publications as part of the contract terms. The report to Defra should include: the scientific objectives as set out in the contract; the extent to which the objectives set out in the contract have been met; details of methods used and the results obtained, including statistical analysis (if appropriate); a discussion of the results and their reliability; the main implications of the findings; possible future work; and any action resulting from the research (e.g. IP, Knowledge Transfer).

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Introduction and background

Sclerotinia sclerotiorum is a major soilborne fungal pathogen causing Sclerotinia disease on many important crops both in the UK and world-wide. In the UK, the disease is a major limiting factor in growing field lettuce and is difficult to control. One reason for this is that the durable resting structures (sclerotia) of the fungus can survive in the soil for several years and are therefore relatively inaccessible to control measures. When sclerotia are brought to the soil surface by tillage operations, they germinate carpogenically to produce mushroom-like structures (apothecia) which release ascospores and these infect plants. Further sclerotia are then formed on diseased plants and are eventually returned to the soil. In some areas, continual disease outbreaks and a failure to adequately control them has led to such an increase in numbers of sclerotia in the soil that disease risk has become too high and land has been taken out of production. Disease levels of just 15% in the UK lettuce crop currently equate to a total loss of £12 M. Sclerotinia disease control relies on applying approved fungicides (iprodione, azoxystrobin, boscalid + pyraclostrobin) to kill the airborne ascospores. However, a major problem is in timing sprays accurately so that disease is controlled and unnecessary applications avoided. This is important as prophylactic spray regimes are expensive, environmentally undesirable and could potentially lead to fungicide resistance developing in Sclerotinia. The aim of this project was to develop a predictive model for Sclerotinia disease based on an understanding of the environmental factors affecting germination of sclerotia to produce apothecia, infection by ascospores and disease development. This builds on previous work (HH1745TFV) where important environmental factors which affect different stages of the S. sclerotiorum life cycle were identified. The development of a forecasting model will allow periods of high disease risk in lettuce crops to be identified and hence enable rational, economic and effective use of current and future foliar fungicides. This work addresses Defra policy requirements promoting a modern, adaptable, diverse and sustainable farming industry as well as a competitive and safe food supply chain which is responsive to the needs of consumers. Significantly, the work will be of direct relevance not only to UK lettuce producers but may also be extended for use in other crops affected by Sclerotinia in the UK such as oilseed rape, potatoes, carrots, celery and sunflower.

Project objectives

1. Derive relationships between environmental factors, Sclerotinia infection and disease development from existing data and develop a preliminary predictive model for infection.

2. Validate predictive model for infection and refine relationships with environmental factors by further field infection studies.

3. Derive a relationship between soil water potential, temperature and apothecial production and develop a preliminary predictive model from existing data.

4. Validate predictive model for apothecial production and refine relationships with environmental factors by further laboratory and field studies.

5. Produce a preliminary Sclerotinia disease forecasting model by combining apothecial development and infection models.

General materials and methods

S. sclerotiorum isolates and production of sclerotia

The two isolates of S. sclerotiorum used in this study were derived from sclerotia on diseased lettuce plants grown on a Cheshire peat soil (Turbary Moor Series; isolate 13; IMI 390053) and a Norfolk silty clay loam (Blacktoft Series; isolate TM; IMI 390054). Original isolations were made by surface sterilising sclerotia in 50% v/v sodium hypochlorite and 70% ethanol for 4 min with agitation followed by two washes in sterile distilled water (SDW) for 1 min. Sclerotia were then bisected, placed on potato dextrose agar (PDA; Oxoid) and incubated at 20°C. Stock sclerotia of each isolate were collected from 4 week old cultures grown on PDA and were stored at 10°C. To provide sclerotia for experiments, stock sclerotia were surface sterilised and bisected as before, placed on PDA and incubated for 4 days at 20°C. Agar plugs (approx. 3 mm2) from the edge of the colonies were then used to inoculate sterile wheat grain (25 g wheat grain, 50 g water autoclaved at 121°C for 15 min) in 500 ml conical flasks. Flasks were incubated at 18°C for 4 weeks after which sclerotia had formed and matured. Generally, flasks were then incubated at 4°C for 4 weeks as a cold conditioning treatment for the sclerotia to ensure rapid and adequate carpogenic germination in experiments (Sansford & Coley-Smith, 1992). After this period, sclerotia were wet sieved to recover those between 2 and 5 mm and the wheat grain was floated off. Finally the sclerotia were dried in an air-flow overnight after which they were used immediately in experiments.

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Production of apothecia and collection of ascospores

Pre-conditioned sclerotia (30) of isolate 13 were evenly placed on 100 g John Innes No 1 compost (GEM gardening, Accrington, UK; pasteurised at 110 °C for 30 min) in clear plastic boxes (600 ml volume, Malsar Kest Ltd, London, UK). They were then covered with a further 30 g pasteurised compost (0.5 cm depth), the boxes sealed and placed in a cooled glasshouse at 15-22 °C or in a controlled environment cabinet at 15°C (12 h light / dark). The compost moisture content was maintained at 30% (w/w) by adding an appropriate amount of water initially and maintaining the weight of each box by further additions each week. Mature apothecia appeared after 4-5 weeks when ascospores could be collected. Ascospores were collected by opening the lid of a plastic box containing the apothecia and trapping ejected ascospores on a 9 cm Whatman No. 1 filter paper in a Buchner funnel attached to a suction pump. Ascospores from several boxes of apothecia were collected on a single filter paper. Ascospores on filter papers were stored at 4°C in a desiccator for no longer than 2 months before use. To produce suspensions of ascospores in water, four filter papers with ascospores were agitated in 1 L distilled water until the filter papers broke up. The suspension was filtered through 2 layers of muslin to give concentrations ranging from 5 x 104 to 1 x 105 ascospores/ml. Viability was always greater than 90%, as checked by observing germination of ascospores on PDA under the microscope after plating appropriate dilutions of the ascospore suspensions and incubating for 24h at 20°C.

Objective 1: Derive relationships between environmental factors, Sclerotinia infection and disease development from existing data and develop a preliminary model for infection

1.1 Infection model concept and structure

A preliminary model of S. sclerotiorum ascospore infection and disease development was developed which assumed that the proportion of a lettuce crop diseased was a function of the infection probability and the effective spore concentration. Conceptually the surface of the lettuce is divided into an ‘active’ infection court area (ICA) where the microclimate is conducive for spore germination and infection with the remaining area considered ‘inactive’ (no germination). This concept was developed from previous research where Sclerotinia disease was always seen first at the stembase of lettuce plants and could occur in the absence of surface wetness and at low ambient relative humidity (RH). As S. sclerotiorum spores require high RH or free water to germinate, this suggested that a microclimate around the stem base (i.e. the ICA) could support these conditions (Young et al., 2004). As the ICA is a function of RH, its proportion of the lettuce area can vary over time. At very high RHs, all of the lettuce plant including the leaves may be included in the modelled ICA reflecting the observation that Sclerotinia lesions are only ever seen on leaves (as opposed to the stem base) at very high RH. RH therefore controls the levels of germination opportunity in the model through increasing or decreasing the ICA. As well as the effect of RH, the infection model also responds to changes in ascospore density and air temperature and progresses stepwise. At each time step, spores in the ICA get the opportunity to germinate, but their success is a function of temperature. Those that do not germinate in this time step are discounted. This reflects the observation that ascospores that have started to germinate may not survive even short breaks in wetness which has a negative impact on S sclerotiorum infection (Philips, 1994). Spores landing outside the model ICA await the next time-step and an increase in RH in order to get the opportunity to germinate. Spores can therefore accumulate at each time step on the ‘dry/low RH’ part of the lettuce unless the temperature exceeds a threshold at which they are destroyed. Effective spores are modelled as those that have germinated multiplied by a spore density factor to account for the observation from previous work that the likelihood of infection does not increase linearly with spore number. The rate of infection and disease development is modelled as one process conceptually and is also a function of temperature and RH. Each lettuce in the modelled crop is assigned a ‘fitness’ factor so that the population of the lettuce can reach the point of observed disease at varying times. The steps involved in the infection model are summarised in Table 1.

Step Model inputs Model outputs1. lettuce growth radiation and temperature lettuce area2. spore deposition spore concentration spore density on plant3. infection court area rainfall / relative humidity active ICA4. spore germination temperature, ICA, spore density germinating spores in ICA5. number of plants infected temperature, germinating spores in ICA proportion of infected lettuce6. disease development temperature, RH, lettuce fitness proportion of diseased lettuce

Table 1: Summary of steps involved in the S. sclerotiorum infection model with model inputs and outputs.

1.2 Defining infection model parameter values through controlled environment experiments

Parameter values for the preliminary S. sclerotiorum infection model were defined using data generated through controlled environment (CE) experiments where the effects of RH and temperature on ascospore germination and infection for one isolate of S. sclerotiorum (isolate 13) were determined on lettuce. These experiments

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edinvestigated the following: 1) effect of temperature on spore germination on lettuce leaves with leaf wetness; 2) effect of temperature on the rate of disease development and proportion of plants infected at constant RH for pre-infected lettuce; 3) the effect of both RH and temperature on rate of infection and disease development. In these experiments lettuce plants (4–6 weeks old with five to six fully expanded leaves; 21 plants per treatment) were inoculated with S. sclerotiorum ascospores either as spore suspensions in water (5x104 - 1x105 spores ml−1) or as dry spores applied directly to lettuce. The latter method involved producing apothecia in boxes as described previously. Lettuce were then placed in inoculation chambers and the lids of the boxes opened to allow the puffing of ascospores from the apothecia onto the plants. Spore density was assessed by placing pieces of acetate within the lettuce to capture spores, which were then enumerated under the microscope. The incidence of Sclerotinia disease was assessed every 2-3 days in all experiments.

1.3 Results of controlled environment experiments

Effect of temperature on spore germination

Germination of S. sclerotiorum ascospores on lettuce plants exposed to continual leaf wetness (RH = 100%) was assessed over a 24 h period at 5, 10, 15, 20, 25 and 30°C (Young et al, 2004) and the experiment repeated three times. Analysis of the data showed that temperature had a significant effect on the final proportion of spores germinating as well as on the germination rate. Germination occurred between 5 and 25°C with no germination at 30°C. There was a polynomial relationship between temperature and the final proportion of spores germinated within 24 h (Fig.1), which was used in the model. In further laboratory experiments, ascospore germination was also shown to occur only when RH > 97% (data not shown).

Figure 1: Proportion of S. sclerotiorum ascospores germinating at different temperatures within 24 h (RH = 100%).

Effect of temperature Sclerotinia disease development and proportion of lettuce infected at constant RH

Replicate experiments were carried out to establish a relationship between temperature and Sclerotinia disease development (post-infection) at constant RH for use in the infection model. To ensure infection of lettuce occurred, plants were inoculated with an ascospore suspension and placed in CE cabinets at 100% RH (leaf wetness) at 20°C for 24 h. After this period, plants were moved to temperature regimes of 8, 11, 16, 22 and 27°C at 85% RH and incidence of Sclerotinia disease recorded over time. Results showed that the rate of disease development was similar at 16-27°C but was much slower at 8 and 11°C (Fig. 2).

Figure 2: Effect of temperature on Sclerotinia disease development at 85% RH.

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Effect of relative humidity on Sclerotinia infection and disease development at constant temperature

Replicate experiments were carried out to establish a relationship between RH and Sclerotinia infection and disease development at constant temperature. In contrast to the previous experiment there was no initial 24h period of 100% RH to ensure infection. Lettuce plants were inoculated with dry ascospores and maintained at 20°C in CE cabinets at 50, 60, 70, 80, 90 and 100% RH. Results showed that infection and disease development was slow at 50-70% RH with the most rapid infection at 100% RH (Figure 3).

Effect of temperature on Sclerotinia infection and disease development at constant RH

To establish the effect of interactions between temperature and RH on Sclerotinia infection and disease development, sets of experiments were carried out where lettuce inoculated with dry ascospores were maintained at temperatures of 7, 10, 15, 20 and 25°C at 60, 80 or 100% RH. Results showed that particularly at 100% RH, infection and disease development were very rapid and the relative effect of temperature was reduced (Fig. 4) compared to that at lower RH.

1.4 Constructing the infection model using controlled environment data

The overall infection model (Table 1) was constructed stepwise using parameters derived from the CE experiments described in sections 1.2 and 1.3. The models involved in each step of the overall infection model were constructed as follows and details of the equations are reported as in Appendix 1.

Lettuce growth model (Appendix 1 equations 1-4)

Lettuce growth was modelled as a simple logistic function for lettuce heart diameter based on effective day degrees based on functions published by Wurr et al., (1992). This function grows the lettuce from the onset of hearting and assumes that on the planting date that the lettuce are established seedlings with only a few leaves. Heart diameter and whole lettuce diameter are calculated in the model so that the proportion of spores landing on the lettuce can be estimated. The function was calibrated against lettuce (cv. Calgary) growth data collected from a field site in Cheshire in 2000 (Young et al., 2003).

Spore deposition model (Appendix 1, equations 5-6)

At each time step, incoming spores are divided into those that land in the ICA and those that do not. The spores in the ICA are given the opportunity to germinate; spores that do not germinate within the time interval do not get another opportunity and are discarded. In the field situation, cool mornings with prolonged leaf wetness and high RH in the lettuce stem base would provide conditions suitable in the model for infection and hence would promote a greater modelled ICA. Spores landing outside the ICA can accumulate at each time step unless a threshold temperature of 27°C is exceeded, at which point they are destroyed.

Infection court area (ICA) model (Appendix 1, equation 7)

The ICA factor is a function of RH and determines the fraction of the plant area in which spores can germinate. The relationship was derived from experiments where RH was varied at a constant temperature and the proportion of plants diseased observed (section 1.4).

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Figure 4: Effect of temperature on Sclerotinia infection and disease development at 100% RH.

Spore germination model (Appendix 1, equation 8)

This model describes the proportion of spores landing in the ICA that germinate and is a function of temperature. This was derived from CE experiment where spore germination was assessed on lettuce leaves at 100% RH.

Infection model (Appendix 1, equations 9-11)

The probability of a single spore infecting is a function of temperature which was estimated from the CE experiments where infected plants were maintained at different temperatures at 85% RH (Figure 2). Infection probability is also enhanced by RH and this function was derived from CE experiments where inoculated plants were maintained at 20°C at different RH levels.

Disease development model (Appendix 1, equations 12-14)

Once a lettuce plant is infected, the rate of disease development until visible symptoms are seen is a function of temperature and humidity and was derived using the same CE data as for infection.

Objective 2. Validate predictive model for infection and refine relationships with environmental factors by further field infection studies2.1 Validation of the Sclerotinia infection model with existing field infection data

In 2000, ten plantings of lettuce in a growers field at Rixton, Cheshire were monitored for Sclerotinia disease (Young et al., 2004). Hourly weather data consisting of temperature, rainfall and radiation recorded by a logger were used as input for the infection model. Observations of apothecia had been recorded in the field each week and spore deposition was estimated as 5000 spores m-2 weighted by the numbers of observed apothecia at each time step. Apothecia were not considered to be present at time steps other than those corresponding to the observation day. Using the infection model, estimates of the proportion of lettuce plants diseased were calculated over time for each of the ten lettuce plantings and compared with observed levels of Sclerotinia disease. Given the difficulty in estimating spore deposition, the model predicted the timing of disease onset and also the final percentage plants infected for most of the lettuce plantings (Figure 5). In particular the model captured the change in disease incidence across the growing season, especially the lower levels of Sclerotinia observed in later plantings (7-9).

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Figure 5: Predicted Sclerotinia disease by the infection model (lines) compared with observed disease (symbols) for ten plantings of lettuce in Cheshire in 2000 (data for plantings 1 and 10 not shown as no disease observed or predicted).

2.2 Validation of the Sclerotinia infection model with further field infection experiments

In 2003-2005 field experiments with lettuce at different growth stages were carried out at ADAS Terrington, Norfolk. Every year, crops of lettuce were grown, each with plots representing a succession of weekly or bi-weekly planting times (20 lettuce plants per plot, 5 replicate plots per treatment). Five weeks after the first planting, lettuce from all the different planting times (which were therefore at different growth stages) were sprayed until run-off with S. sclerotiorum ascospore suspensions (5x104 - 1x105 spores ml−1) using an MDM Oxford precision (CO2) sprayer with a 4 nozzle boom. Sclerotinia disease and crop growth (number of leaves, heart diameter, total plant diameter) were recorded regularly and weather data was collected with an in-crop logger. A summary of the field experiments is shown in Table 2.

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Year Lettucevariety

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2003 Calgary 3 14/3, 21/5, 7/7 5 1, 2, 3, 4, 52004 Calgary 3 7/4, 10/5, 29/6 3 1, 3, 52005 Stallion 2 5/4, 16/5 3 1, 3, 5

Table 2: Summary of field experiments for Sclerotinia infection model validation 2003-2005.

2.3 Results of further field infection experiments

Lettuce growth

Data for plant heart diameter and total plant diameter were used to develop and validate the lettuce growth model (see section 1.2). Lettuce growth followed a similar pattern for all crops in each year, with variation mainly due to seasonal temperature. Example results from 2003 are shown in Table 3.

Week post-planting Date assessed Mean number

of leaves *Mean total diam. (cm)

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Week 0 14-Apr 4 6.4 0Week 1 21-Apr 5.8 7.8 0Week 2 28-Apr 7.8 10 0Week 3 5-May 8.2 13.3 0Week 4 12-May 9 16.6 <1Week 5 19-May * 19.2 <1Week 6 29-May * 27.7 6.6Week 7 3-Jun * 33.0 8.7Week 8 9-Jun * 35.9 11.8Week 9 16-Jun * 37.8 11.8Week 10 23-Jun * 39.1 13.2* not assessed after heart formation

Table 3. Example of lettuce growth data, ADAS Terrington, 2003 for crop 1, first plantingSclerotinia diseaseIn 2003, there was little or no Sclerotinia disease in any of the plantings in the three inoculated crops (<4% plants infected). In 2004, crop 1 the three plantings had no Sclerotinia disease while in crop 2, plantings corresponding to 1, 3 and 5 week old plants at inoculation had 10, 0 and 3% Sclerotinia disease, respectively. In crop 3, Sclerotinia incidence in all three plantings was 1-3%. In 2005, crop 1 had <2% Sclerotinia disease in the three plantings while in crop 2 there was 4, 8 and 10% Sclerotinia disease, respectively in plantings corresponding to 1, 3 and 5 week old plants at inoculation.

Validation of the Sclerotinia infection model

Weather data, inoculation dates and lettuce growth data were used to run the Sclerotinia infection model for each of the crops and plantings at ADAS Terrington in 2003 to 2005. In each case it was found that the estimated spore deposition from the artificial inoculations resulted in significantly more disease than was actually observed in the field which was generally low (<10%) or zero in each crop. Where there was zero disease was observed, the model predicted more than 50% disease. A scalar was therefore introduced to the model to reduce the effectiveness of the spores in the applied suspension and was calibrated across all experiments to best represent the general character of disease response (Fig. 6). The necessity for this calibration scalar therefore indicates that the results of the controlled environment studies were not immediately transferable to the field. This may be related to the diurnal extremes of temperature or relative humidity but detailed examination of the hourly weather records did not reveal any correlation. With this factor in place, on the occasions when observed disease was relatively high (Fig. 6, planting 1) the model was able to reproduce the time of onset and rate of development of disease, but not the absolute level of final disease. For the remaining crops and plantings where there was 0-5% Sclerotinia, the model also predicted low or no disease. In some cases the infection model did not always predict disease when disease was measured (Fig. 6, planting 3). However, given the low level of disease, this might easily be attributed to a small error (10%) in the model predictions. Overall, there were very not enough occasions of high measured disease to validate the model for field application with any confidence.

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Fig 6. Predicted and observed Sclerotinia disease development in the three plantings of crop two at ADAS Terrington, Norfolk, 2005. Note that in planting two there was no observed disease.

Fig 6. Predicted and observed Sclerotinia disease development in the three plantings of crop two at ADAS Terrington, Norfolk, 2005. Note that in planting two there was no observed disease.

Main findings Objectives 1-2

Germination of S. sclerotiorum ascospores only occurs above 97% RH Controlled environment experiments showed that Sclerotinia infection and disease development

occurs for a wide range of RH levels and is optimum at 15-25°C. The occurrence of Sclerotinia disease at low RH suggests that microsites exist on lettuce plants

where the environment is conducive to infection A Sclerotinia infection model was developed using the concept of an active infection court area to

describe the disease development observed in controlled environment experiments When applied to field data, the infection model could predict levels of Sclerotinia when the

observed incidence was > 5-10% but was less accurate when disease levels were low or zero

Objective 3. Derive a relationship between soil water potential, temperature and apothecial production and develop a preliminary predictive model from existing data

A previous Defra project (HH1745TFV) has shown that temperature and water potential are the key factors limiting germination of sclerotia from two S. sclerotiorum isolates (13 and TM) to produce apothecia and an activity range for these parameters was identified in the laboratory. These data were used to produce a preliminary model for germination of isolate 13 sclerotia (Clarkson et al., 2004). Briefly, this preliminary model was based on a thermal time approach which involves accumulating time where the temperature is above a certain base (day degrees) to predict when germination of the sclerotia will occur. However, the calculation was modified according to results obtained in the laboratory such that there was no contribution if the temperature was above or below certain test thresholds or if the water potential was below a certain test threshold.

Objective 4. Validate predictive model for apothecial production and refine relationships with environmental factors by further laboratory and field studies

4.1 Validation of a preliminary predictive model for apothecial production

The preliminary predictive model for germination of sclerotia derived in Objective 3 was validated for S. sclerotiorum isolate 13 using existing field experiment data from a Cheshire site where sclerotia were buried bi-weekly from March-August and germination monitored with concurrent recording of weather data (Clarkson et al., 2004). The thermal time model was run and predictions generated for when 10 or 50% of the buried sclerotia would germinate. These predictions were compared with actual observed time to 10 or 50% germination in the burials and the accuracy of these predictions was summarised by the root mean square error value. Results from existing laboratory data suggested that thermal time would only accrue at temperatures above a threshold of 0-5°C and below 25°C and for a soil water potential >-100 kPa but using these values as model constraints did not improve predictions compared to using no constraints (data not shown). Testing a factorial set of different threshold values, the best predictions of germination were obtained for temperatures ≥ 5°C and ≤ 16°C and a soil water potential threshold of ≥ -200 kPa. These thresholds did not entirely agree with the laboratory results, in particular the upper limit of 16°C. Furthermore, the model could not account for the poor germination observed in field experiments for burials of sclerotia made from mid-May. These discrepancies clearly suggested that further work was required to improve the germination model from sclerotia.

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4.2 Further laboratory and field studies

In order to address the problems encountered while validating the preliminary sclerotial germination model, a hypothesis was proposed which suggested that the degree of cold conditioning of S. sclerotiorum sclerotia was critical in their subsequent germination response to temperature. Sclerotia buried in the field for the model validation experiments in 2000 and 2001 had been conditioned for 28 days at 4°C in wheat grain flasks, but if this was an inadequate period, they would require further cold conditions in order to complete this phase before they could germinate quickly. This would explain why sclerotia buried in the field from mid-May generally did not germinate in the same year (as soil temperatures would be too high) and why a relatively low upper temperature threshold of 16°C gave better predictions of germination in the thermal time model than a laboratory derived threshold of 25°C. To test this hypothesis, laboratory experiments involving transfer of sclerotia between two temperature regimes were conducted (Stage 1 and Stage 2 temperatures) and germination was monitored. Different durations at Stage 1 temperatures largely represented the conditioning process of the sclerotia while Stage 2 mainly represented the germination period. However, depending on the temperature regime, germination might occur by the end of Stage 1 or conditioning could still be continuing at the start of Stage 2. The aim of these temperature transfer experiments was to derive times for conditioning and germination phases at a range of temperatures which could be incorporated into an improved predictive model for germination of sclerotia. Experiment 1 was set up to test the effect of temperature on germination of sclerotia (Stage 2) in soil following periods of time in wheat grain at 4°C (Stage 1) whereas Experiments 2 and 3 tested a matrix of temperature regimes for Stage 1 and 2 for sclerotia in soil which was considered to be more applicable with the natural situation in the field.

Experiment 1

Sclerotia of S. sclerotiorum isolates 13 and TM were produced in wheat grain flasks at 20°C and then transferred (in the same flasks) to 4°C for durations between 0 and 417 days (Stage 1). After harvesting and drying, the sclerotia were buried in boxes containing a peat soil (isolate 13) or silty clay loam (isolate TM) at a depth of 1 cm, a water potential of -9 kPa and temperatures of 10, 13, 15 or 18°C (Stage 2). Germination of the sclerotia to produce stipes or apothecia was recorded weekly. For each Stage 1 / Stage 2 treatment there were three replicate boxes each containing 30 sclerotia arranged in a grid pattern.

Experiment 2

Sclerotia of S. sclerotiorum isolates 13 and TM produced in wheat grain flasks were buried in bulk batches (110 sclerotia in mesh bags at 3 cm depth) in a peat soil (isolate 13) or silty clay loam (isolate TM) at a water potential of -9 kPa for 0, 30, 50, 75 and 100 days at 4, 7, 10, 13, 15 and 20°C (Stage 1). After this period, sclerotia were buried at 1 cm depth in boxes containing the peat or silty clay loam at 15°C (Stage 2) and germination monitored as before.

Experiment 3

Sclerotia of S. sclerotiorum isolates 13 and TM were buried in mesh bags in soil as in experiment 2 for 0, 30, 50, 75 and 100 days at 4°C (Stage 1). After this period, sclerotia were buried in boxes containing the peat or silty clay loam at 10, 13, 15, 18 and 25°C (Stage 2) and germination monitored as before.

Field experiments

Field experiments similar to those carried out in the previous Defra project HH1745TFV were also continued in 2003 and 2004 to generate further data for validation of an improved model for sclerotial germination. These were carried out at sites in Cheshire (isolate 13) and Norfolk (isolate TM) and involved burying pre-conditioned sclerotia (28 days at 4°C) at regular intervals throughout the year and monitoring germination to produce apothecia. At each burial, four replicates of one hundred sclerotia were buried in a grid pattern at 1cm depth at each field site. Soil temperature and water potential were recorded using a data logger. In 2005, experiments were repeated with a limited set of burials (January-June) but with unconditioned sclerotia.

Statistical analyses

Analyses were based on the cumulative number of sclerotia which had germinated over time for all the laboratory experiments 1-3. A lognormal distribution was fitted to the cumulative germination curves for each temperature regime. This implies that the logarithm of times to germination follow a normal distribution whose parameters relate to the mean germination time as shown in Appendix 2, equation 15. Mean times to germination for the lognormal distribution were calculated for each treatment regime examined in the experiments. Analysis of the data was by analysis of variance on the summary statistics, transformed as appropriate (inverse of time (i.e. rate) and angular transformation of maximum percentage germinated). Each analysis included both S. sclerotiorum isolates thus generating high order interactions to be used as error terms. Duration at Stage 1 and temperature

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effects were partitioned into linear and quadratic terms. F tests on the variance ratios were used to identify the important trends. All effects described in the results are significant at P<0.001.

4.3 Results from laboratory studies

Overall, the lognormal distribution could be fitted to the germination curves in all experiments. However in a few cases, where germination was poor or very slow the distribution could not be fitted and hence the mean time to germination could not be calculated. These treatments are indicated in the results of each experiment.

Experiment 1

The effect of Stage 1 duration at 4°C and Stage 2 temperature on the mean time to germination for sclerotia of S. sclerotiorum isolates 13 and TM is shown in Fig. 7. Overall, longer conditioning durations at 4°C resulted in fewer days to germination at the temperatures tested (10, 13, 15 and 18°C). This trend was highly consistent for both isolates with mean times to germination ranging from 155 days for unconditioned sclerotia to 29 days after 417 days at Stage 1 over all temperatures. However, there was little decrease in time to germination for periods beyond 100 days at 4°C. Stage 2 temperature also had a big linear effect with mean germination times decreasing from 91.2 to 47.7 days between 10°C and 18°C over both isolates. Over all the treatments, maximum germination was higher for isolate TM (70.2%) than for isolate 13 (58.6%) and there was a also a linear trend of germination with temperature decrease (over all treatments and both isolates) from 68.4% to 57.6% between 10°C and 18°C. It was also noted that no germination of sclerotia ever occurred in the wheat grain flasks, even for those held at 4°C for more than 400 days.

Figure 7: Effect of duration at 4°C (Stage 1) in wheat grain flasks on germination of S. sclerotiorum sclerotia at different Stage 2 temperatures in soil for isolates 13 and TM. Bars = standard error of the mean.

Experiment 2

The mean time to germination of sclerotia at 15°C (Stage 2) for both S. sclerotiorum isolates decreased significantly with duration at Stage 1 and increased with Stage 1 temperature (Fig. 8). These effects were clearer for isolate TM as times to germination were generally longer. For isolate 13, germination began during Stage 1 at 13 and 15°C for 100 days duration so accurate mean times to germination could not be derived as monitoring did not occur during this period. For isolate TM, poor germination following Stage 1 durations of 30 and 50 days at 15°C meant that mean times to germination were also not derived for these treatments. There were no significant effects of treatments on maximum percentage germination for either isolate.

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Figure 8: Effect of Stage 1 temperature and duration on time to germination of S. sclerotiorum sclerotia at 15°C (Stage 2) for isolates 13 and TM. Bars = standard error of the mean.

Experiment 3

The mean time to germination of sclerotia for both S. sclerotiorum isolates decreased with Stage 2 temperature for all Stage 1 durations at 4°C (Figure 9). If sclerotia had no time at 4°C (i.e. stage 1 duration =0), times to germination in Stage 2 were very large and for some Stage 1 temperature regimes (isolate 13 at 10°C and isolate TM at 10, 13 and 18°C) mean times to germination could not be derived. There was also little or no germination for either isolate at 25°C which again limited the data set. For Stage 1 durations > 0 days, there was generally a decrease in time to germination with increasing period at 4°C but this was only significant for isolate 13. There was a non-linear effect of Stage 2 germination temperature on maximum germination of both isolates due to a sharp reduction in germination between 15°C and 18°C. For isolate TM, maximum percentage germination increased as the duration at Stage 1 increased.

Figure 9: Effect of duration at 4°C (Stage 1) on time to germination of S. sclerotiorum sclerotia at 10, 13, 15, 18 and 25°C (Stage 2) for isolates 13 and TM. Bars = standard error of the mean.

4.4 An improved model for germination of S. sclerotiorum sclerotia

The results from the laboratory experiments appeared to support the hypothesis that the degree of conditioning of S. sclerotiorum sclerotia was an important factor in their subsequent germination. The results also indicated that low temperatures were optimum for this process. A new improved model to describe the germination of sclerotia was therefore derived using the results from Experiments 2 and 3. This was based on the assumption that a conditioning phase must be completed before germination could occur with the rates of both processes being solely dependent on temperature when soil moisture was not limiting. Rates were derived for the conditioning and germination phases for sclerotia from both S. sclerotiorum isolates at different temperatures in order to describe the mean germination times calculated from Experiments 2 and 3. To develop this model, a curve was derived for each phase relating rate to temperature. Because there was a lack of prior information about the shape of each curve, the model was initially developed by estimating a separate value for rate of conditioning or germination at different temperatures and plotting the rates against temperature. The model, which had 16 parameters (for temperatures 4, 7, 10, 13, 15, 18, 20, 25°C in each phase), was fitted using the fitnonlinear directive in GenStat (Payne et al, 2004). This gave an indication of the shape of the two curves and was used to provide starting values for fitting a model. Following this analysis it was found that the shape of the curves could be described by a modified exponential curve for conditioning rate and an Arrhenius curve for germination rate for both S. sclerotiorum isolates (Figs. 10a and 10b). The model accounted for 75% of the variation in the observed data for

SID 5 (2/05) of 26Figure 10a: Fitted curve for conditioning rate for S. sclerotiorum isolates 13 and TM.

Figure 10b: Fitted curve for germination rate for S. sclerotiorum isolates 13 and TM.

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isolate 13 and 83% of the variation for isolate TM. Fitted values could only be accurately estimated for temperatures up to 20°C as germination was poor at 25°C and hence data at this temperature were limited. The model shows that there is quick completion of the conditioning phase at 5°C for both isolates (Fig. 10a) with isolate 13 much faster than TM. The rate of conditioning then quickly decreases as temperature increases. The model also shows that the germination phase is fastest at 18-20°C, with isolate 13 consistently quicker than TM, especially at higher temperatures (Fig. 10b).

Equations for the modified exponential curve representing the rate of conditioning and the Arrhenius curve representing the rate of germination of sclerotia are shown in Appendix 2, equations 16 and 17.

Validation of the sclerotial germination model using field data

The improved model for germination of S. sclerotiorum sclerotia was validated using 5 years of field data for isolates 13 (Cheshire field site) and TM (Norfolk field site) where sclerotia were buried at different times and germination to produce apothecia monitored (2000, 2001, 2003, 2004, 2005). Simulation curves for the observed germination in the field were produced by applying the model using the weather data recorded by the data logger. Predicted times for 10% (T10) and 50% (T50) germination of sclerotia were compared with the corresponding observed times. In order to apply the model, the following procedures were carried out.

Calculating the degree of conditioning of sclerotia in flasks

In all years except 2005, pre-conditioned sclerotia (28 days at 4°C in wheat grain flasks) were buried in the field so before the improved germination model could be applied, an estimate of the degree of conditioning achieved during this time for each S. sclerotiorum isolate was required. This was deduced by applying the model for germination in soil derived from experiments 3 and 4 (Appendix 2, equation 17) to the data in experiment 1. This allowed the degree of conditioning and germination that occurred in the flasks to be calculated for the different durations at 4°C (Stage 1, experiment 1). This calculation (Appendix 2, equation 18) showed that for sclerotia kept at 28 days in flasks at 4°C, the fraction of conditioning achieved was 0.45 for isolate 13 and 1.12 for isolate TM. Hence sclerotia of isolate 13 were partially conditioned when buried in the field and TM sclerotia were fully conditioned.

Simulating germination curves for burials of sclerotia in the field and calculating predicted germination times

The weather data collected from the field and used to run the germination model were soil temperature and water potential recorded at half-hourly intervals. Equation 16 (Appendix 2) was applied to the soil temperatures to calculate the progress of conditioning by accumulating the rates (divided by 48) at each half-hourly temperature record. Conditioning was assumed to be complete when the accumulation reached 1. For burials where sclerotia had been pre-conditioned for 28 days at 4°C, this process was only necessary for the data for S. sclerotiorum isolate 13 at the Cheshire field site (with the appropriate starting value of 0.45) and was not required for isolate TM at the Norfolk field site where sclerotia were fully conditioned (see previous section). Once accumulated rates of conditioning had reached 1, germination rates were then accumulated using equation 17 (Appendix 2). The mean time to germination, (M, Appendix 2, equation 15) of the lognormal distribution, then occurs when this accumulation reached 1. Before simulation curves for the cumulative germination of buried sclerotia in the field could be produced, the variability in the germination of sclerotia required examination and this was done using data from experiment 3. Graphs of the standard deviation against the mean of the normal distribution for these data (s and m, Appendix 2, equation 15) showed that s was constant (s = 0.1417) for all values of m for both S. sclerotiorum isolates. This value of s was then used with the generated values of M to calculate m in order to

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produce a normal distribution for each burial of sclerotia in the field. Based on this, a simulation curve was produced by calculating the percentage of sclerotia which would have germinated at each recording time. The predicted T10 and T50 times for germination were then calculated for comparison with the equivalent observed times interpolated from the observed germination curves in the field.

Thresholds of temperature and water potential applied to the germination model

Germination of S. sclerotiorum sclerotia did not occur above 25°C in previous experiments (HH1745TFV) and conditioning appeared to be negligible above 20°C in experiments 2 and 3. Therefore, rates were set to zero above these upper temperature limits when the conditioning and germination models (equations 16 and 17, Appendix 2) were run using the soil temperature data from the field. Previous work showed that conditioning and germination of sclerotia only occur when soil moisture is not limiting and therefore a threshold for water potential was also required. This was derived from the field experiment data and was incorporated analogously to the temperature thresholds. To find the water potential threshold that gave the best fit for each field site and in each year, the model was run with a grid of water potentials at 0.25 kPa intervals. The predicted T50 times generated by the model were then compared with the equivalent observed T50 times of the buried sclerotia and the residual mean square error (rmse) calculated from the difference between the observed and predicted values for each burial in each year. The water potential value which minimised the rmse was chosen as the optimum threshold value. For some burials, satisfactory predictions could not be made because the predicted mean time was beyond the end of the recording period and hence no weather data was available.

4.5 Results using the improved model for germination of S. sclerotiorum sclerotia

Overall, the model successfully reproduced the pattern of germination from sclerotia buried at different times of year observed at Cheshire and Norfolk for all data sets. This was evident from the close association of the simulation curves for germination produced by the model and the observed germination curves of the buried sclerotia. Examples from each field site are shown in Figure 5 for 2004.

Figure 5: Simulation curves generated by the germination model (lines) compared with actual germination of S. sclerotiorum sclerotia (symbols) buried at Cheshire and Norfolk field sites at different times of year.

Optimum water potential thresholds that gave the best results for prediction of germination varied between -6.5 and -12.25 kPa for S. sclerotiorum isolate 13 and -4.0 and -4.25 kPa for isolate TM (Table 4). During the data analysis it was also found that small changes in threshold value could have a large effect on predictions. The accuracy of predictions by the model of the exact time after burial of when T10 and T50 would occur for sclerotia in the field experiments in all years was therefore somewhat variable (Table 4) but were generally better for burials early in the year. However, prediction of whether burials would achieve T10 or T50 germination or not germinate at all was highly significant (P<0.001) as determined by a chi squared (Χ2 ) test summarised in Table 5. Approximately 80% of the 64 burials at each site over 5 years were correctly predicted for germination or non germination.

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Table 4: Observed and predicted times for germination of sclerotia of S. sclerotiorum isolate 13 (Cheshire) and isolate TM (Norfolk) for 5 years of field experiments. a water potential threshold; b observed time to 10% germination; c predicted time to 10% germination; d observed time to 50% germination; e predicted time to 50% germination; f /g difference between predicted and observed times for 10% / 50% germination; * no germination/prediction

S. sclerotiorum isolate 13, Cheshire field site S. sclerotiorum isolate TM, Norfolk field site

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diff T10f

diff T50g

03/04/00 -6.75 85.9 98.8 93.5 118.4 -12.9 -24.9 05/04/00 -4.25 161.8 165.6 * 198.6 -3.8 * 17/04/00 -6.75 77.9 90.9 117.2 108.9 -12.9 8.3 17/04/00 -4.25 159.1 164.2 * 197.0 -5.1 * 01/05/00 -6.75 90.3 88.8 122.4 106.5 1.5 15.9 08/05/00 -4.25 * * * * * * 15/05/00 -6.75 81.4 88.0 111.1 105.5 -6.5 5.6 17/05/00 -4.25 * * * * * * 29/05/00 -6.75 83.7 91.5 109.3 109.7 -7.7 -0.4 30/05/00 -4.25 * * * * * * 12/06/00 -6.75 * 111.2 * 133.3 * * 12/06/00 -4.25 * * * * * * 27/06/00 -6.75 * * * * * * 27/06/00 -4.25 * * * * * * 10/07/00 -6.75 * * * * * * 10/07/00 -4.25 * * * * * * 25/07/00 -6.75 * * * * * * 24/07/00 -4.25 * * * * * * 07/08/00 -6.75 * * * * * * 07/08/00 -4.25 * * * * * * 08/03/01 -12.25 99.0 87.3 109.2 104.6 11.7 4.6 05/03/01 -4.25 135.1 166.1 * 199.2 -31.0 * 22/03/01 -12.25 87.3 93.1 111.5 111.6 -5.8 -0.1 19/03/01 -4.25 152.6 159.9 * 191.8 -7.3 * 05/04/01 -12.25 77.6 104.6 120.1 125.4 -27.0 -5.3 02/04/01 -4.25 131.9 153.7 169.9 184.3 -21.8 -14.4 19/04/01 -12.25 107.1 97.2 124.8 116.5 9.9 8.3 17/04/01 -4.25 133.3 148.9 153.3 178.6 -15.6 -25.3 03/05/01 -12.25 102.4 90.1 126.0 108.0 12.3 18.0 01/05/01 -4.25 138.3 144.5 * 173.3 -6.2 * 17/05/01 -12.25 * 97.4 * 116.8 * * 15/05/01 -4.25 * 136.3 * 163.4 * * 31/05/01 -12.25 * 99.0 * 118.7 * * 01/06/01 -4.25 * * * * * * 14/06/01 -12.25 95.3 98.7 * 118.4 -3.4 * 13/06/01 -4.25 * * * * * * 28/06/01 -12.25 87.6 * * * * * 28/06/01 -4.25 * * * * * * 12/07/01 -12.25 * * * * * * 09/07/01 -4.25 * * * * * * 26/07/01 -12.25 * * * * * * 23/07/01 -4.25 * * * * * * 09/08/01 -12.25 * * * * * * 07/08/01 -4.25 * * * * * * 23/12/02 -6.50 143.7 124.3 146.3 149.0 19.4 -2.7 18/12/02 -4.25 153.9 160.7 182.8 192.7 -6.8 -9.9 04/03/03 -6.50 114.0 73.7 117.7 88.4 40.3 29.3 03/03/03 -4.25 * 125.4 * 150.3 * * 17/03/03 -6.50 102.0 65.2 107.6 78.1 36.8 29.5 17/03/03 -4.25 109.0 122.1 145.2 146.4 -13.1 -1.2 31/03/03 -6.50 88.4 58.0 93.7 69.5 30.4 24.2 31/03/03 -4.25 116.4 145.0 161.4 173.9 -28.6 -12.5 14/04/03 -6.50 78.1 69.2 103.8 83.0 8.9 20.8 15/04/03 -4.25 114.3 133.7 * 160.4 -19.4 * 28/04/03 -6.50 66.0 122.7 91.6 147.1 -56.7 -55.5 29/04/03 -4.25 92.6 122.6 * 147.0 -30.0 * 12/05/03 -6.50 77.3 127.6 118.0 152.9 -50.3 -34.9 15/05/03 -4.25 * 136.4 * 163.6 * * 27/05/03 -6.50 102.4 * 141.0 * * * 28/05/03 -4.25 * * * * * * 10/06/03 -6.50 * * * * * * 09/06/03 -4.25 * * * * * * 23/06/03 -6.50 * * * * * * 24/06/03 -4.25 * * * * * * 08/07/03 -6.50 * * * * * * 07/07/03 -4.25 * * * * * * 21/07/03 -6.50 * * * * * * 23/07/03 -4.25 * * * * * * 04/08/03 -6.50 * * * * * * 04/08/03 -4.25 * * * * * * 17/12/03 -8.00 133.7 124.3 145.3 149.1 9.4 -3.8 17/12/03 -4.00 191.4 177.2 214.5 212.5 14.2 2.0 21/01/04 -8.00 116.9 125.9 152.8 150.9 -9.0 1.9 14/01/04 -4.00 170.6 174.5 * 209.3 -3.9 * 12/02/04 -8.00 127.9 115.3 167.9 138.3 12.6 29.6 09/02/04 -4.00 166.5 157.8 * 189.3 8.7 * 02/03/04 -8.00 73.3 110.8 106.2 132.9 -37.5 -26.7 03/03/04 -4.00 133.0 143.1 170.4 171.6 -10.1 -1.2 17/03/04 -8.00 93.1 102.0 105.1 122.4 -8.9 -17.3 15/03/04 -4.00 154.3 135.9 199.1 162.9 18.4 36.2 29/03/04 -8.00 82.7 98.6 94.0 118.3 -15.9 -24.3 29/03/04 -4.00 129.3 129.3 150.9 155.1 0.0 -4.2 13/04/04 -8.00 79.4 101.9 108.7 122.2 -22.5 -13.5 13/04/04 -4.00 125.4 127.7 * 153.1 -2.3 * 28/04/04 -8.00 93.0 96.6 117.6 115.8 -3.6 1.8 26/04/04 -4.00 160.6 127.3 * 152.6 33.3 * 11/05/04 -8.00 82.5 104.5 131.8 125.3 -22.0 6.5 10/05/04 -4.00 * 124.8 * 149.6 * * 25/05/04 -8.00 91.0 105.2 * 126.1 -14.2 * 24/05/04 -4.00 * 120.0 * 143.8 * * 09/06/04 -8.00 72.2 95.9 * 115.1 -23.7 * 07/06/04 -4.00 * * * * * * 24/06/04 -8.00 80.2 87.5 * 104.9 -7.3 * 21/06/04 -4.00 * * * * * * 07/07/04 -8.00 88.6 * * * * * 05/07/04 -4.00 * * * * * * 22/07/04 -8.00 * * * * * * 19/07/04 -4.00 * * * * * * 02/08/04 -8.00 * * * * * * 02/08/04 -4.00 * * * * * * 08/09/04 -8.00 * * * * * * 15/09/04 -4.00 * * * * * * 05/10/04 -8.00 * * * * * * 06/10/04 -4.00 * * * * * * 14/12/04 -11.75 156.0 144.9 * 173.8 11.1 * 20/12/04 -4.00 162.2 158.7 * 190.2 3.5 * 11/01/05 -11.75 130.9 121.8 149.8 146.1 9.1 3.7 11/01/05 -4.00 164.2 144.0 * 172.7 20.2 * 08/02/05 -11.75 111.8 117.7 145.1 141.2 -5.9 3.9 03/02/05 -4.00 143.0 131.2 * 157.3 11.8 * 01/03/05 -11.75 87.7 * 101.9 * * * 28/02/05 -4.00 118.7 124.7 * 149.5 -6.0 * 15/03/05 -11.75 109.8 * * * * * 14/03/05 -4.00 121.0 * * * * * 29/03/05 -11.75 * * * * * * 29/03/05 -4.00 134.7 * * * * * 12/04/05 -11.75 82.8 * 114.8 * * * 11/04/05 -4.00 129.1 * * * * * 26/04/05 -11.75 95.6 * 120.3 * * * 26/04/05 -4.00 * * * * * * 10/05/05 -11.75 106.3 * * * * * 09/05/05 -4.00 * * * * * * 24/05/05 -11.75 * * * * * * 23/05/05 -4.00 * * * * * * 07/06/05 -11.75 * * * * * * 07/06/05 -4.00 * * * * * * 21/06/05 -11.75 * * * * * * 20/06/05 -4.00 * * * * * *

Cheshire Norfolk10% germ 50% germ 10% germ 50% germ

Predicted & observed 33 28 32 35Not predicted, not observed 20 24 24 9

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Predicted, not observed 3 3 4 20Not predicted, observed 8 4 4 0Total burials 64 64 64 64Significance (Χ2 test) P< 0.001 P< 0.001 P< 0.001 P< 0.001

Table 5: Number of occasions where correct () or incorrect () predictions of 10 or 50% germination of sclerotia were made by the germination model for S. sclerotiorum sclerotia at the Cheshire and Norfolk field sites.

Main findings Objectives 3-4

Conditioning rate of sclerotia in soil was fastest at 4-5°C and decreased rapidly above 6°C Germination rate of sclerotia in soil increased with temperature up to 18-20°C with little or no

germination at 25°C The response of conditioning and germination to temperature was different for the two S.

sclerotiorum isolates tested Germination models based on the effect of soil temperature and water potential on conditioning and

germination processes was derived for two S. sclerotiorum isolates The germination models could simulate the pattern of germination of sclerotia observed in field

experiments and could predict germination / non-germination well The accuracy of the germination models in predicting the actual times to 10% or 50% germination of

sclerotia in the field was variable

Objective 5. Produce a preliminary Sclerotinia disease forecasting model by combining apothecial development and infection models

The sets of models describing the carpogenic germination of S. sclerotiorum sclerotia to produce apothecia and also the germination and infection of lettuce by ascospores and disease development were combined and programmed in Visual Basic to enable running of models both separately and together from sets of weather data recorded by loggers in the field. This represents a preliminary forecasting model for Sclerotinia disease and is also a useful tool for running strategic scenarios with weather data to potentially determine times of the year where disease risk is high.

Discussion and implications of results

Temperature and relative humidity (RH) were found to be the main drivers for spore germination and infection by S. sclerotiorum in this study and was suggested by previous work. Spore germination was shown to only occur at RH >97% and this is consistent with other published research (Abawi & Grogan, 1975, 1979; Tu, 1989; Grogan & Abawi, 1975). However, when lettuce were inoculated with dry ascospores, Sclerotinia disease still occurred at RH levels as low as 50% although the rate of development was much slower than at higher RH. This apparent inconsistency led to the concept of an active infection court area (ICA) at the lettuce stem base where the microclimate results in high RH levels suitable for infection. This was consistent with the observation made in this and other studies that Sclerotinia disease always appears at the stem base (Newton & Sequeira, 1972; Abawi & Grogan, 1979). Temperature affected the rate of spore germination and disease development and the optimal range of 15-27°C is in agreement with other studies on bean (Boland & Hall, 1987; Phillips, 1994) and lettuce (Young et al., 2004). The concept of an active ICA the size of which is modified by ambient RH and the processes of germination, infection and disease progress being modified by temperature and RH were fundamental to the development of the Sclerotinia infection model in this study. In addition, spore deposition and lettuce area were also included as important factors in the model. When applied to field data, the infection model could successfully reproduce the pattern of disease development observed in a succession of plantings within a commercial crop in Cheshire in 2000 where Sclerotinia levels were high but was less effective in predicting disease when levels were low as observed in artificially inoculated field experiments in 2003-2005.

The development of the infection model for Sclerotinia has important implications. The idea of an ICA based around the lettuce stem base and modified by ambient RH explains why some Sclerotinia disease can be observed in commercial lettuce crops even after relatively dry weather as well as the observation that disease is more prevalent after canopy closure when ambient RH and hence the ICA is greater. The model also suggests that infection by ascospores will almost always occur under most weather conditions but that the number of infections and the rate of subsequent disease development should be reduced by consistent high temperatures and low RH.

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Soil temperature and water potential were confirmed in this study to be the major factors affecting the carpogenic germination of S. sclerotiorum sclerotia to produce apothecia as demonstrated previously (Hao et al., 2003; Clarkson et al., 2004). For rapid germination of sclerotia it was shown that a cold conditioning period was required as observed by other workers (Smith & Boland, 1989; Sansford & Coley-Smith, 1992) but through modelling, this is the first time that this process has been quantified and shows that that temperatures around 5°C are most effective. Derivation of the model for subsequent germination (post-conditioning) showed that the temperature response of the S. sclerotiorum isolates tested was largely in agreement with recent research with other S. sclerotiorum isolates (Sun & Yang, 2000; Hao et al., 2003). However, the two S. sclerotiorum isolates did have slightly different temperature responses for both conditioning and germination resulting in separate carpogenic germination models being derived for each. When the models were applied to field data, they could successfully reproduce the pattern of germination observed in field experiments where sclerotia were buried at different times of year, in particular the decline in germination for sclerotia buried in May/June. However, the accuracy of the models in predicting the exact time after burial when apothecia would appear was variable. This is most likely because of the effect of, and the difficult in measuring, soil water potential. Previous laboratory work had shown that germination of sclerotia does not occur below -100 KPa (Clarkson et al., 2004) and it seemed appropriate therefore to impose a water potential threshold to the germination model when applied in the field (see section 4.4). However the most suitable threshold was found to be between -4 and -12 kPa for the S. sclerotiorum isolates. This apparent discrepancy between the field and laboratory data was probably because the probes used in the field give an average water potential over a depth of 10 cm. Sclerotia which only germinate in the top few cm, are therefore exposed to drier conditions than those recorded by the probe. Water potential values were also found to be very variable even over small areas in the field. This variation when combined with the finding that small changes in the threshold water potential have a large effect on the germination model explains why predictions of when sclerotia germinated in the field were not always accurate. Therefore it was also generally found that the most accurate predictions of germination were for sclerotia buried in December-March when the soil was nearly always wet and a soil water potential threshold was not applicable.

The derivation of the conditioning and germination models have practical implications. Sclerotia formed on infected crops even early in the lettuce season are unlikely to be able to germinate and pose a threat to following crops as the increasing soil temperatures would not be conducive to conditioning and may also be inhibitory to subsequent germination during the summer. However, newly formed sclerotia once over-wintered will be fully conditioned and ready to germinate once soil temperatures increase in the following spring, as will sclerotia surviving from previous seasons. It is envisaged therefore, that in a practical situation, sclerotia brought to the soil surface (where they can germinate) by tillage operations are likely to be fully conditioned and therefore the conditioning part of the germination model would not be required.

Overall, this work has produced a suite of models that can simulate germination of S. sclerotiorum sclerotia to produce apothecia and also subsequent ascospore infection and Sclerotinia disease development in lettuce. The combined models represent a preliminary forecasting system with the potential to predict when Sclerotinia disease occurs in commercial lettuce crops so that fungicides can be deployed appropriately.

Potential future work

Although the project has produced a preliminary disease forecasting system for Sclerotinia, further work is required to produce a robust model that can be used in commercial lettuce crops. Spore deposition is a major input in the infection model and currently can only be estimated. To address this, it would be necessary to determine a relationship between the density of apothecia and the numbers of spores falling on nearby lettuce leaves. Also, previous work showed that there was a relationship between ascospore density and the final level of Sclerotinia disease, but the lack of significant disease levels when comparable high spore densities were used in artificially inoculated lettuce in the field requires further investigation. The models for the germination of sclerotia for two isolates of S. sclerotiorum highlighted the variation in response to temperature by the fungus and is an important factor to consider when forecasting the disease. Infection studies only used one isolate of S. sclerotiorum so there may also be variation in temperature response for pathogenicity. Such genetic variation in S. sclerotiorum is being investigated in a current Defra project (HH3230SFV). Finally, further work is now required to determine how effective the current model is in forecasting Sclerotinia disease in commercial lettuce crops and how decisions on fungicide applications will be made and an application has been made to the HDC for this purpose. As the Sclerotinia forecasting model is a simulation, disease risk in a lettuce crop at any one time can only be determined by running the model with up to the minute weather data and determining the risk of apothecia being present as well as whether infection conditions have occurred. In this situation, a quick response in terms of fungicide application may be required. To overcome this, the use of future weather forecasts and previous scenarios using historic data sets should also be considered as useful tools in the future.

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Appendix 1: Equations used in the Sclerotinia infection model

A1.1 Lettuce growth model

Effective day degrees for lettuce growth are calculated each hour as:

(1)

where is a constant and in this application is set at unity; = Effective Day Degrees; is incident solar

radiation (MJ m-2) and is temperature (degrees Celsius).

(2)

where subscript denotes each hour; is the hour of lettuce planting; is the hour of lettuce harvest;

is the cumulative effective day degree hours.

The model assumes that on the planting date that the lettuce are established seedlings with only a few leaves. The heart diameter is then set to begin accumulating at 100 effective degree days after the planting date. If the size of the lettuce is different from this default at planting this information can be added as an input to the model and the initial hearting diameter is reset from the default of 3 cm. The initialised value of 100 is estimated from field experimental data collected at the Rixton field site in the growing season of 2000.

(3)

where = 0.6095; =5.43095 ; =0.010624 and = 43.55; is the lettuce heart diameter at each (ith)

timestep (mm) and is the cumulative effective day degree hours.

Plant diameter is then estimated as

(4)

Where is heart diameter (cm) and is plant diameter (cm);

A1.2 Spore deposition model

(5)

(6)

where is the number of spores in the infection court area at the (ith) timestep; is the number of spores

on the remaining lettuce area at the (ith) timestep; is number of spores on the remaining lettuce area at the

previous timestep; is the lettuce plant area (m2); is the infection court area factor; is the number of spores per m2 (input variable) and is a weighting factor on the spore density and is set to unity as default.

A1.3 Infection court area model

(7)

where is infection court area and is relative humidity (%).

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A1.4 Spore germination model

(8)

where is the proportion of spores (in the infection court area) germinating and is temperature (degrees Celsius).

A1.5 Infection model

(9)

(10)

where is the probability of infection as a result of temperature variation ; is the probability infection factor

and is relative humidity (%) and is the probability of infection.

The germination factor , probability of infection factor , effective spore concentration are multiplied

together to give the number of infections.

(11)

where is the prop; is the probability of infection; is the effective spore concentration; is infection

court area function and is the plant area (m2) and. is the proportion of spore (in the infection court area) germinating.

A1.6 Disease development model

(12)

(13)

(14)

where is the disease development time as a function of temperature (days); is disease development

factor (dimensionless) as a function of relative humidity ; is a fitness factor, drawn from a normal distribution

with a mean of 0 and standard deviation of 1, assigned to each lettuce (dimensionless); is the standard

deviation on the mean time to disease development; is the final estimate of disease development time.

Appendix 2: Equations used in the Sclerotinia germination model for sclerotia

A2.1 Mean germination time for S. sclerotiorum sclerotia

(15)

where M is the mean time to germination and m and s are the mean and standard deviation of the corresponding normal distribution.

A2.2 Modified exponential curve representing conditioning rate of sclerotia

(16)

where is rate of conditioning per day, a, b and k are constants and T is temperature (°C).Note: conditioning rate was set to 0 above 20°C and set to a maximum at 4°C.

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A2.3 Arrhenius curve representing germination rate of sclerotia

(17)

where is rate of germination per day, and are constants and T is temperature (°C)

Parameters for exponential and arrhenius curves, with their standard errors, for each equation and for each S. sclerotiotum isolate are shown in the table below. For isolate 13, the exact value of parameter b did not affect the fit of the model except at 4°C so it was set to an arbitrary value of 1000.

S. sclerotiorum isolate 13 S. sclerotiorum isolate TMParameter Parameter value Standard error Parameter value Standard error

0.03273 0.00395 0.01056 0.0011000 * 1.28 1.611.498 0.398 0.435 0.118

31.12 4.36 24.8 3.38

-10138 1236 -8422 961

A2.4 Calculation of the degree of conditioning of sclerotia in flasks

The fraction of the germination process ( ) achieved after sclerotia were removed from the flasks and buried in soil at different temperatures (Stage 2, experiment 1, section 4.4) was calculated from the mean germination time for each temperature using equation (11). Then, the following relationship applies:

(18)

where is the duration of stage 1 and and are the times to condition and germinate at 4°C in flasks

respectively. Linear regression of on allow and to be estimated.Note: data for sclerotia kept at 4°C for more than 101 days was omitted from the regression calculation because the mean germination times indicated that sclerotia held at Stage 1 for 100 days or more only ever achieved a fraction of approximately 0.6 of their germination (where 1.0 = germination occurs) whilst in the flasks.

The estimates were 62 and 25 days for and 86 and 136 days for for isolates 13 and TM respectively. Therefore for sclerotia kept at 28 days in flasks at 4°C, the fraction of conditioning achieved was 28/62= 0.45 for isolate 13 and 28/25=1.12 for isolate TM.

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References to published material9. This section should be used to record links (hypertext links where possible) or references to other

published material generated by, or relating to this project.

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Publications related to the project

Abawi GS, Grogan RG, 1975. Source of primary inoculum and effects of temperature and moisture on infection of beans by Whetzelinia sclerotiorum. Phytopathology 65, 300-309.

Abawi GS, Grogan RG, 1979. Epidemiology of diseases caused by Sclerotinia species. Phytopathology 69, 899-904.

Boland GJ, Hall R, 1987. Epidemiology of white mold of white bean in Ontario. Canadian Journal of Plant Pathology 9, 218-224.

Clarkson JP, Phelps K, Whipps JM, Young CS, Smith, JA, Watling M, 2004. Forecasting Sclerotinia disease on lettuce: toward developing a prediction model for carpogenic germination of sclerotia. Phytopathology 94, 268-279.

Grogan RG, Abawi GS, 1975. Influence of water potential on growth and survival of Whetzelina sclerotiorum. Phytopathology 65,122-138.

Hao JJ, Subbarao KV, Duniway JM, 2003. Germination of Sclerotinia minor and S. sclerotiorum sclerotia under various soil moisture and temperature combinations. Phytopathology 93, 443-450.

Newton HC, Sequeira L, 1972. Ascospores as the primary infective propagule of Sclerotinia sclerotiorum in Wisconsin. Plant Disease 56, 798-802.

Payne R, Murray D, Harding S, Baird D, Soutar D, Lane P, 2003. GenStat® for Windows 7th Edition, VSN International, UK.

Phillips AJL, 1994. Influence of fluctuating temperatures and interrupted periods of plant surface wetness on infection of bean leaves by ascospores of Sclerotinia sclerotiorum. Annals of Applied Biology 124 , 413-427.

Sansford CE, Coley-Smith JR, 1992. Production and germination of sclerotia of Sclerotinia sclerotiorum. Plant Pathology 41, 154-156.

Smith EA, Boland GJ, 1989. A reliable method for the production and maintenance of germinated sclerotia of Sclerotinia sclerotiorum. Canadian Journal of Plant Pathology 11, 45-48.

Sun P, Yang XB, 2000. Light, temperature, and moisture effects on apothecium production of Sclerotinia sclerotiorum. Plant Disease 84, 1287-1293.

Tu C, 1989. Modes of primary infection caused by Sclerotinia sclerotiorum in navy bean. Microbios 57, 85-91.

Wurr DCE, Fellows JR, Hiron RWP, Antill DN, Hand DJ (1992). The development and evaluation of techniques to predict when to harvest iceberg lettuce heads. Journal of Horticultural Science 67, 385-393.

Young CS, Clarkson JC, Smith JA, Watling M, Phelps K, Whipps, JM, 2004. Environmental conditions influencing Sclerotinia sclerotiorum infection and disease development in lettuce. Plant Pathology 53, 387-397.

Publications generated by the project

Refereed papers

Clarkson JP, Staveley J, Phelps K, Young CS, & Whipps JM, 2003. Ascospore release and survival in Sclerotinia sclerotiorum. Mycological Research, 107, 213.

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Publications related to the project

Abawi GS, Grogan RG, 1975. Source of primary inoculum and effects of temperature and moisture on infection of beans by Whetzelinia sclerotiorum. Phytopathology 65, 300-309.

Abawi GS, Grogan RG, 1979. Epidemiology of diseases caused by Sclerotinia species. Phytopathology 69, 899-904.

Boland GJ, Hall R, 1987. Epidemiology of white mold of white bean in Ontario. Canadian Journal of Plant Pathology 9, 218-224.

Clarkson JP, Phelps K, Whipps JM, Young CS, Smith, JA, Watling M, 2004. Forecasting Sclerotinia disease on lettuce: toward developing a prediction model for carpogenic germination of sclerotia. Phytopathology 94, 268-279.

Grogan RG, Abawi GS, 1975. Influence of water potential on growth and survival of Whetzelina sclerotiorum. Phytopathology 65,122-138.

Hao JJ, Subbarao KV, Duniway JM, 2003. Germination of Sclerotinia minor and S. sclerotiorum sclerotia under various soil moisture and temperature combinations. Phytopathology 93, 443-450.

Newton HC, Sequeira L, 1972. Ascospores as the primary infective propagule of Sclerotinia sclerotiorum in Wisconsin. Plant Disease 56, 798-802.

Payne R, Murray D, Harding S, Baird D, Soutar D, Lane P, 2003. GenStat® for Windows 7th Edition, VSN International, UK.

Phillips AJL, 1994. Influence of fluctuating temperatures and interrupted periods of plant surface wetness on infection of bean leaves by ascospores of Sclerotinia sclerotiorum. Annals of Applied Biology 124 , 413-427.

Sansford CE, Coley-Smith JR, 1992. Production and germination of sclerotia of Sclerotinia sclerotiorum. Plant Pathology 41, 154-156.

Smith EA, Boland GJ, 1989. A reliable method for the production and maintenance of germinated sclerotia of Sclerotinia sclerotiorum. Canadian Journal of Plant Pathology 11, 45-48.

Sun P, Yang XB, 2000. Light, temperature, and moisture effects on apothecium production of Sclerotinia sclerotiorum. Plant Disease 84, 1287-1293.

Tu C, 1989. Modes of primary infection caused by Sclerotinia sclerotiorum in navy bean. Microbios 57, 85-91.

Wurr DCE, Fellows JR, Hiron RWP, Antill DN, Hand DJ (1992). The development and evaluation of techniques to predict when to harvest iceberg lettuce heads. Journal of Horticultural Science 67, 385-393.

Young CS, Clarkson JC, Smith JA, Watling M, Phelps K, Whipps, JM, 2004. Environmental conditions influencing Sclerotinia sclerotiorum infection and disease development in lettuce. Plant Pathology 53, 387-397.

Scientific presentations

John Whipps, Feb 2003. Developing strategies for control of Sclerotinia, Sclerotinia workshop, 8th International Congress of Plant Pathology, Christchurch, New Zealand.

John Whipps, Nov 2003. Plant pathology and microbiology research at HRI, Presentation to INRA visitors.

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