Plant disease forecasting may be a management system used to predict the incidence or modification in severity of plant diseases. At the sector scale, these systems are utilized by growers to create economic selections regarding disease treatments for management. Typically the systems raise the grower a series of questions about the susceptibility of the host crop, and incorporate current and forecast atmospheric condition to create a recommendation. Generally a recommendation is created regarding whether or not disease treatment is important or not. Sometimes treatment may be a pesticide application.
Forecasting systems are primarily based on assumptions regarding the pathogen's interactions with the host and surroundings, the disease triangle. The target is to accurately predict when the 3 factors - host, surroundings, and pathogen - all interact in such a fashion that disease will occur and cause economic losses.
In most cases the host will be suitably outlined as resistant or prone, and therefore the presence of the pathogen could typically be moderately ascertained primarily based on previous cropping history or maybe survey information. The surroundings is sometimes the issue that controls whether or not disease develops or not. Environmental conditions could confirm the presence of the pathogen in a very specific season through their effects on processes like overwintering. Environmental conditions conjointly have an effect on the power of the pathogen to cause disease, e.g. A minimum leaf wetness length is needed for gray leaf spot of corn to occur. In these cases a disease forecasting system makes an attempt to outline when the surroundings are conducive to disease development.
Good disease forecasting systems should be reliable, simple, cost-effective and applicable to several diseases. As such they're normally solely designed for diseases that are irregular enough to warrant a prediction system, instead of diseases that occur per annum that regular treatment ought to use. Forecasting systems will solely be designed if there's conjointly an understanding on the particular disease triangle parameters.
Examples of disease forecasting systems
Forecasting systems could use one in all many parameters so as to figure out disease risk, or a mixture of things. one in all the primary forecasting systems designed was for Stewart's Wilt and primarily based on winter temperature index as low temperatures would kill the vector of the disease thus there would be no outbreak.
An example of a multiple disease/pest forecasting system is that the EPIdemiology, PREdiction, and PREvention (EPIPRE) system developed within the Netherlands for winter wheat that targeted on multiple pathogens. USPEST.org graphs risks of assorted plants diseases primarily based on weather forecasts with hourly resolution of leaf wetness.
Forecasting models are typically primarily based on a relationship like easy linear regression where x is employed to predict y. Alternative relationships will be modelled using population growth curves.The growth curve that's used can rely on the character of the epidemic. Polycyclic epidemics like potato late blight are sometimes best modelled by using the logistic model, whereas monocyclic epidemics is also best modelled using the monomolecular model. Correct selection of a model is important for a disease forecasting system to be helpful.
Plant disease forecasting models should be totally tested and validated when being developed. Interest has arisen lately in model validation through the quantification of the economic prices of false positives and false negatives, where disease prevention measures is also used when unnecessary or not applied when required respectively.The costs of those sorts of errors got to be weighed fastidiously before deciding to use a disease forecasting system.
Source :
http://en.wikipedia.org/wiki/Plant_disease_forecasting
Forecasting systems are primarily based on assumptions regarding the pathogen's interactions with the host and surroundings, the disease triangle. The target is to accurately predict when the 3 factors - host, surroundings, and pathogen - all interact in such a fashion that disease will occur and cause economic losses.
In most cases the host will be suitably outlined as resistant or prone, and therefore the presence of the pathogen could typically be moderately ascertained primarily based on previous cropping history or maybe survey information. The surroundings is sometimes the issue that controls whether or not disease develops or not. Environmental conditions could confirm the presence of the pathogen in a very specific season through their effects on processes like overwintering. Environmental conditions conjointly have an effect on the power of the pathogen to cause disease, e.g. A minimum leaf wetness length is needed for gray leaf spot of corn to occur. In these cases a disease forecasting system makes an attempt to outline when the surroundings are conducive to disease development.
Good disease forecasting systems should be reliable, simple, cost-effective and applicable to several diseases. As such they're normally solely designed for diseases that are irregular enough to warrant a prediction system, instead of diseases that occur per annum that regular treatment ought to use. Forecasting systems will solely be designed if there's conjointly an understanding on the particular disease triangle parameters.
Examples of disease forecasting systems
Forecasting systems could use one in all many parameters so as to figure out disease risk, or a mixture of things. one in all the primary forecasting systems designed was for Stewart's Wilt and primarily based on winter temperature index as low temperatures would kill the vector of the disease thus there would be no outbreak.
An example of a multiple disease/pest forecasting system is that the EPIdemiology, PREdiction, and PREvention (EPIPRE) system developed within the Netherlands for winter wheat that targeted on multiple pathogens. USPEST.org graphs risks of assorted plants diseases primarily based on weather forecasts with hourly resolution of leaf wetness.
Forecasting models are typically primarily based on a relationship like easy linear regression where x is employed to predict y. Alternative relationships will be modelled using population growth curves.The growth curve that's used can rely on the character of the epidemic. Polycyclic epidemics like potato late blight are sometimes best modelled by using the logistic model, whereas monocyclic epidemics is also best modelled using the monomolecular model. Correct selection of a model is important for a disease forecasting system to be helpful.
Plant disease forecasting models should be totally tested and validated when being developed. Interest has arisen lately in model validation through the quantification of the economic prices of false positives and false negatives, where disease prevention measures is also used when unnecessary or not applied when required respectively.The costs of those sorts of errors got to be weighed fastidiously before deciding to use a disease forecasting system.
Source :
http://en.wikipedia.org/wiki/Plant_disease_forecasting