Meteobot® helps you control plant diseases. The weather data from your field or orchard can be automatically transmitted to disease models, which can generate forecasts for plant disease risks.
The models, used by Meteobot®, are dynamic. They take into account the current climatic conditions, as well as the available pathogens, accumulated as a result of previous infection events. Under the same climatic conditions, but with a different amount of potential infection, the forecasted disease risk is different. Precise models show you that rain events do not always cause infections, or that a lot of rain does not lead to severe infections.
How do models work?
The models provide forecasts for diseases and pests by taking into account the conditions, which favour their occurrence and development. The models contain rules and algorithms, obtained from scientific research and many field trials. These rules and algorithms take into account rain, temperature, air humidity, leaf wetness, solar radiation, etc. The data comes directly from the weather stations and it is used to automatically calculate a forecast for the expected moment and intensity of the next infection.
As you know, for example, fungus diseases develop when it is warm and humid. In practice, however, the conditions and dependencies between diseases and insects are often much more complex, than we might think. Even if we know them, we can make full use of them only if weather data is obtained each hour and is automatically processed by a computer.
Beside weather data, many other factors are taken into account. Wheat and barley models, for example, contain built-in information about the phenological stages of plant development and the yield risk. The resistance (or tolerance) to diseases of different crop varieties is also incorporated in the models. The previous year crop, the type and amount of the applied fertilizer, etc. are take in mind as well. Other models, such as those for apples, estimate the residual effect of plant protections products on certain pathogens. All of these make the disease forecasts as accurate as possible.
The built-in information in the models has been checked and validated many times through field trials in many countries all over the world. Before being applied in practice, the models have been tested in various climatic zones and situations, under varying disease pressure. Some of them, such as the apple scab model, for example, have been developed in the end of the 1990s and have been validated on several continents since then. The wheat and barley models are based on scientific research and field trials since 1997. Others are more recent and have been introduced into practice after having showed stable results.
Based on all of these features, the models provide a dynamic forecast for the expected moment and the degree of the infection risk.
The purpose is to spray only when necessary and with the most adequate product.
How do models actually help?
- Spray only if and when necessary. “Calendar sprays”, or spraying according to the term of the plant protection product, are a common practice. For example, if a pesticide’s effective term in two weeks, farmers spray every two weeks. You don’t need to do that anymore! With the models, you can spray if and when there actually is an infection risk. It is most effective to apply a product right before or immediately after an infection event; and in any case before the development of the disease. In this moment the disease agent is most vulnerable and the effect of spraying is the strongest.
- Choose the most adequate product. You don’t have to spray “blindfolded” or “just in case” any more. The models take into account your individual situation and give you a forecast about the specific disease risk for your crops. This way you can reduce or diversify the use of broad-spectrum plant protection products. Instead, you can use the most effective product, which will have the most adequate action – preventative or curative – in your specific case.
- Avoid resistance. When a product is used too often against certain disease agents, over time they become resistant to it. This can be the case, for example, with system (curative) fungicides or broad-spectrum products. Resistance can also occur in case of “calendar sprays”. If the infection has occurred towards the end of the product’s term, when its protective effect is weaker, some of the pathogens may survive. The surviving microorganisms may become more resistant against this product during next applications. To avoid resistance, with the help of the models, you can reduce the usage of certain pesticides, or rotate them. This way you can keep a “trump”, which you can use when really necessary. Besides, by spraying in the right moment, you minimize the chance of surviving pathogens, which may have become resistant.
- Less stress in busy periods. The disease risk forecast gives you additional peace of mind especially in spring, when the staff and machinery are busy with many activities (fertilizing, planting, herbicide application, etc.) If there is no risk at the moment, you can postpone the spraying by several days, in order to finish your other important tasks.
- Integrated plant protection. The models help you use the most adequate products and spray only as needed to bring the risk under the harmful threshold.
- Bio-production. When spraying on time, you can use allowed fungicides and avoid the use of restricted products.
- Wider use of contact or generic products. The timely forecast enables you to use more contact (than curative) products, especially on perennials. This way you decrease the probability of resistance and of pesticide residue in the farm produce. Besides, when you know what the risk is in your particular situation, you can make wider use of generic plant protection products.
In case you would like the make full use of your Meteobot® functionality regarding disease risk management, you could order:
- A license for disease model integration for 1 year– 125 EUR (excl. VAT);
- Historical weather data package for your fields for 1 year backwards– 60 EUR (excl. VAT);
The historical data is necessary for the model to work correctly, even if the weather station is installed after the date of seeding or after the start of the growing season.