Forecast models

VIPS present forecast models for more than 20 pests and diseases, and a spesial application for dealing with weeds in cereals are included.

Warning models for the following pest&diseases are active the comming season:

Fruit and berries

Potatoes and vegetabels

Cereals

Weeds

 

Fruit and berries

Codling moth, traditional model

Description

The model helps managing the first generation of codling moth, i.e. the timing of measures against eggs and larvae produced by overwintered individuals. It has two components:

  1. Assessing the risk of oviposition and, if high risk, setting a date for the start of oviposition
  2. Calculate the heat sum since estimated start of oviposition. This can then be compared to the one required for eggs to start hatching. 

Heat sum calulation of time for first adult appearance in the spring, and recommended time for deployment of pheromone monitoring traps.

The oviposition risk assessment

The following local input data are needed for this (manual) assessment:

  1. Hourly orchard temperatures (1-2 m above ground) around sunset (twilight)
  2. Catches of male codling moths in pheromone traps (one trap per orchard)
  3. Date of 90-95 % petal fall for late cultivars like ‘Lobo’ or ‘Aroma’

Knowledge about the damage level in the previous year is also valuable.

VIPS provides a table of temperatures at 19-23 hrs for selected weather stations the last 7, 14 or 21 days, the other input must be provided by the user. If weather stations are placed in an open area (and not in an orchard), the given temperatures should be increased with one degree. 

The risk that oviposition has started is considered as high if weekly catch of codling moth exceeds 10-20 individuals after the 90-95 % petal fall AND the sunset temperature has been 14 °C or more for at least three days in the period. High temperatures, high damage last year and big trap catches are all factors that increase the risk. Conversely, in northern areas like Norway, no catch in the pheromone trap means that the risk of oviposition is neglible.

Biofix= The day after the third day of sunset with hourly mean temperature > 13 °C. This date is entered into the heat sum calculation below.

Later in the season, provided that codling moth females are still present (they die 2-3 weeks after emergence, but are later to appear than males), sunset temperatures govern the oviposition pattern. 

 

The calculation of first hatch

Input: Biofix from the oviposition assessment above.

The model summarizes the day degrees above codling moth developmental zero (10 °C) since Biofix. 

There is also an upper limit of egg development (31-34 °C, or possibly lower), but this is not at the moment incorporated into the VIPS heat sum calculation.

The first eggs hatch at about 90 day degrees after they were laid. For some measures hatching will be a bit too late, for others it is the optimal timing. 

Strong rain during egg hatching will kill many of the larvae before they enter the fruit.

 

Interpretation of warning status

The model does not give an automatic warning status, the risk assessment is done manually (see Description)

 

Applescab and Codling Moth models in RIMpro

RIMpro Cloud Service is an interactive Decision Support System (DSS) for pest and disease management in fruit and wine grape production developed by Marc Trapman i Bio Fruit Advies in Holland. Two forcasting models are run for selected districts in Norway.

 

Apple fruit moth (Argyresthia conjugella)

Description 

The forecasting of apple fruit moth (Argyresthia conjugella) attacks in apple has been ongoing since 1979. Attacks in apple occur due to masting in the main host rowan (Sorbus aucuparia). When too few berries are produced in rowan, compared to the population size of the moth, the moth is forced to seek alternative larval host material and will damage apple crops. When berry production is sufficient, the moth will restrict oviposition to rowan and apple growers have no need for control measures. Based on experiments, the rowan crop needs to be minimum 5 times as large as the apple fruit moth population to restrict oviposition to rowan.

The apple fruit moth overwinters as a pupa in the litter below the host tree. The forecast is based on counts of viable moth larvae in rowan berries the previous year and the amount of rowan berries produced in the last and current year. Each prognosis station consists of 20 marked rowan trees. 

In late summer the number of clusters on each of the marked rowan trees are counted. A sample of 1000 berries and an additional berry sample of about 2 kilos is collected from rowan close to each prognosis station. Both samples are sent for analysis. The larvae emerging from the 1000 berries are counted. The second sample is packed in paper bags together with rolls of corrugated cardboard for pupation. Later in autumn the percentage of parasitation from the larval-pupal parasitoid Microgaster pollitus in these rolls can be estimated. 

In spring the florescences on the same prognosis trees are counted. The warning status presented on VIPS (green/yellow/red) is the expert interpretation of the rowan data collected. For each prognosis station  a “prognosis number” can be calculated, which is the fraction of “minimum number of berries needed” (E) and the “available number of berries” (F). When there are fewer berries available than needed, the fraction E/F is larger than 1 and damage can be expected. The prognosis numbers are not published, because numbers close to 1 need to be interpreted by an expert, taking into account regional patterns and the general population level of the moth. 

There are currently 63 prognosis stations placed in apple growing regions in Norway. The forecasting is a collaboration between the extension service (Norsk Landbruksrådgivning) and NIBIO. 

 

Interpretation of warning status

Green: No risk of attack

Yellow: Intermediate risk of attack

Red: High risk of attack

Local knowledge of hot spots for apple fruit moth attacks, and a general evaluation of nearby prognosis stations is important when interpreting warning status. The warning status is accompanied by a written evaluation and interpretation. 

 

Technical usage

Contact (rbm.varsling@nibio.no) for further details on the biological input data presented in Description. There are no other input data.

 

Model for Leaf blotch disease complex in wheat

Diseases: Stagonospora nodorum blotch (SNB) or Glume blotch, Septoria tritici blotch (STB) and Tan spot, caused by Parastagonospora nodorum, Zymoseptoria tritici og Pyrenophora tritici-repentis, respectively. 

Leaf Blotch

Description

The two models predicting severity of leaf blotch diseases in wheat are regression models made by Dr. Oleif Elen in 2005 and based on a regression model for Septoria tritici blotch in winter wheat from Hansen et al (1994). The starting values for disease development for both models are corrected for by varietal resistance, tillage and crop rotation. The main factor driving the model is the number of days with precipitation and total amount of precipitation over a certain time period. Fungicide applications can be entered by the user and the model will correspond to that according to specified fungicide effectivity factors and adjust the predicted disease severity accordingly. Additionally, the user can enter observed disease severity to adjust the predicted values. Current and 10 day in advance predicted climate data is automatically downloaded from the meterological institute and used for the prediction models. For spring wheat the model starts 7 degree days after and for winter wheat once soil temperature has passed 5C. Parallel to the disease development model, an economic threshold model is run based on the potential yield loss caused by the predicted disease severity. The relationship between disease severity at developmental stage BBCH 70-75 and yield loss was set to be 1 (King et al., 1984).

Interpretation of warning status

Fungicide application is recommended once the disease development graph crosses the economic threshold graph. The model does not include recommendation the choice of fungicides or the dosage. In the graph below, you can see that the predicted disease development curve crosses the economic threshold curve on the 4th of June. This is the time recommended to apply a fungicide 

Input data for the spring wheat model: Variety: Bjarne, climate station (locality): Sande (Vestfold),  sowing date: 15.04.2015, ploughing: 16.04.2015, previous crop: wheat.

Technical usage

The following input data is required:

Wheat cultivar, sowing time, climate station, tillage, previous crop. 

Optional input: Fungicide application and disease severity on 4 leaves of 25 plants 

nDRR30: Number of days with precipitation over 1mm over the last 30 days before the requested date

RR30Se: Amount of precipitation over 30 days, starting 40 days before requested date to 10 days before requested date.

For winter wheat: DD200dg: The date when degree days accumulate to over 200. Starting time to calculate degree days is the date at which the soil temperature is over 5C.

For spring wheat: DD200dg: The date when degree days accumulate to over 200.  Starting time to calculate degree days is 7 days after sowing.

References

Abrahamsen, U. 2016. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka/NIBIO bok 2016 2 (1). 112-119

Abrahamsen, U. 2014. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka 2013/Bioforsk Fokus 9 (1). 123-128.

Abrahamsen, U., Elen, O., Brodal, G. 2013. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka 2013/Bioforsk Fokus 8 (1). 130-135.

Elen, O. 2007. Forecasting models of disease in barely, wheat and oilseed crops in Norway. In NJF 23rd Congress 2007 Trends and Perspectives in Agriculture: 209–210

Hansen JG, Secher BJM, Jørgensen LN, Welling B. 1994. Thresholds for control of Septoria spp in winter-wheat based on precipitation and growth stage. Plant Pathology 43: 183-189. 

 

Contact: Andrea Ficke (andrea.ficke@nibio.no)

More information about Leaf Blotch (no):

http://leksikon.nibio.no/vieworganism.php?organismId=1_617&showMacroOrganisms=false

http://leksikon.nibio.no/vieworganism.php?organismId=1_319&showMacroOrganisms=false

http://leksikon.nibio.no/vieworganism.php?organismId=1_618&showMacroOrganisms=false

 

Model for Scald of barley 

Modell description

Disease caused by Rhynchosporium secalis.

 

Photo: Erling Fløistad

 

Photo: Erling Fløistad

The scald model is a regression model made by Dr. Oleif Elen based on Norwegian disease assessment data. The starting values for disease development corrected for by varietal resistance, tillage and crop rotation. The main factor driving the model is the number of days with precipitation and total amount of precipitation over a certain time period. Fungicide applications can be entered by the user and the model will correspond to that according to specified fungicide effectivity factors and adjust the predicted disease severity accordingly. Additionally, the user can enter observed disease severity to adjust the predicted values. Current and 10 day in advance predicted climate data is automatically downloaded from The Norwegian Meteorological Institute and used for the prediction models. For spring wheat the model starts 7 degree days after and for winter wheat once soil temperature has passed 5°C. Parallel to the disease development model, an economic threshold model is run based on the potential yield loss caused by the predicted disease severity. The relationship between disease severity at developmental stage BBCH 70-75 and yield loss was set to be 1 (King et al., 1984).

Interpretation of warning status

Fungicide application is recommended once the disease development graph crosses the threshold graph. The model does not include recommendation on the choice of fungicides or the dosage. The prognosis is presented both in a chart and in a table. On the chart, the disease value and threshold value are displayed. Values after the white dotted line are based on weather forecasts. The background color of the chart reflects the warning status. The table contains the values from the chart, and then some weather data for more detailed information. If the observed or calculated disease value is greater than the threshold value, a red sign indicates that the use of fungicides should be considered. The threshold value is an expression of the economic profitability of applying fungicides. 

 

Technical usage

The following input data is required: Wheat cultivar, sowing time, climate station, tillage, previous crop. 

Optional input: Fungicide application and disease severity on 4 leaves of 25 plants 

nDRR30: Number of days with precipitation over 1mm over the last 30 days before the requested date

RR30Se: Amount of precipitation over 30 days, starting 40 days before requested date to 10 days before requested date.

For winter wheat: DD200dg: The date when degree days accumulate to over 200. Starting time to calculate degree days is the date at which the soil temperature is over 5°C.

For spring wheat: DD200dg: The date when degree days accumulate to over 200.  Starting time to calculate degree days is 7 days after sowing.

 

References

Abrahamsen, U. 2016. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka/NIBIO bok 2016 2 (1). 112-119

Abrahamsen, U. 2014. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka 2013/Bioforsk Fokus 9 (1). 123-128.

Abrahamsen, U., Elen, O., Brodal, G. 2013. Behandling mot soppsjukdommer I vårhvete etter VIPS-varsel. Jord og Plantekulturboka 2013/Bioforsk Fokus 8 (1). 130-135.

Elen, O. 2007. Forecasting models of disease in barely, wheat and oilseed crops in Norway. In NJF 23rd Congress 2007 Trends and Perspectives in Agriculture: 209–210

Hansen JG, Secher BJM, Jørgensen LN, Welling B. 1994. Thresholds for control of Septoria spp in winter-wheat based on precipitation and growth stage. Plant Pathology 43: 183-189. 

Contact: Andrea Ficke (andrea.ficke@nibio.no) 

 

White Mold or Sclerotinia stem rot on rape seed (Sclerotinia sclerotiorum

This fungal disease is caused by Sclerotinia sclerotiorum and S. subarctica in Norway. 

Foto: S. A

 

Foto:J. Razzaghian

Description

The sclerotinia stem rot (SSR) model is a risk model based on a risk point table published by Twengstrøm et al, 1998. An infection risk is estimated after the user has answered several questions related to SSR infections, such as time of flowering, time since the last cropping of oilseed crops and precipitation over the last 14 days.

Interpretation of warning status

Infection risks are given as low, medium and high after the model is finished running. The user then has to evaluate the need for a fungicide spray based on the infection risk, yield potential and potential yield loss, without any chemical intervention.

Input data: Information on flowering, petal fall, how long it has been since the last time oilseed crops were grown in the field, SSR infection severity the last time oilseedcrops had been grown, canopy density, watering and precipitation over the last 14 days (the user can put in their own weather data or choose to get it automatically downloaded from the closest weather station.)

References

Twengström, E. Sigwald, R. Svensson, Yuen, J., 1998. Forecasting Sclerotinia stem rot in spring sown oilseed rape. Crop Protection, 17:405-411.

Contact

Andrea Ficke (andrea.ficke@nibio.no)