Forecast Modeling

What is it and how does it work?


Forecast Modeling is a series of site-specific data collection and forecast services that estimate the favorabilities for crop-damaging disease/insect conditions. These forecasts are created using field-collected climatic data, geo-referenced weather forecasts, proprietary research, and computer-based mathematical models, which are then delivered to the end-user via the Internet.


How is the data collected?


Using a specially equipped digital weather station, data is downloaded via cellular phone, radio, or the internet. Current local field conditions are automatically collected and sent to a database where they are combined in a ‘patent pending’ method with historic, actual, and forecast conditions to create a mathematical forecast of disease/insect favorability for the next seven (7) days.


Why do computer/mathematical models have value?


Agronomic pests are endothermic, meaning they derive a large portion of the energy needed to progress toward maturity as heat from the environment, rather than from the food they eat. In addition, these “pests” develop through a series of life-cycle stages that are pre-determined and unchangeable. The calendar time it takes to complete a life-cycle stage is variable, depending on the rate of accumulation of heat and in some cases the amount of moisture available during one of these life-cycle stages.

These two details of pest biology along with an understanding of crop physiology and vulnerabilities make it possible to correlate heat accumulation and moisture conditions with pest life-cycle stages and estimate the probable consequences of that life-cycle stage to a particular crop or crop stage.


How are the forecasts created?


Using crop-specific agronomic knowledge, research data/information, and generally accepted GIS modeling principles, a mathematical scenario for the likely development of a specific crop pest is created using both single and multi-variable climatic data. This mathematical model is then compared to a geo-referenced set of known values for each variable of crop and pest life-cycle stage. The resulting information is then correlated to produce a forecast probability that a specific pest poses a potential threat to the current estimated crop stage.


What can you do with the information?


Information regarding the probable threat is delivered as a text and graphics report detailing what has likely happened in the most recent week and what is likely to happen in the forecast week.

Each report is a combination of Year To Date estimates (based on historical data), current conditions, and a forecast of the week ahead. As the end-user accumulates the information in the reports, a picture of the trends in a specific field will begin to develop. These trends may indicate low, normal, or high pest pressure and over time can lead to effective targeting of control applications when and where they are most appropriate.


Why is this process effective?


This process is effective because in the case of many diseases and insect infestations significant yield loss can occur before symptoms are visible to standard field scouting techniques. Because of the close relationship between leaf damage and yield loss, it is vitally important to intervene early in the process.

Early intervention and careful timing limit leaf damage (and the associated yield loss) and often allow for the use of preventive rates of pesticide/fungicide, possibly reducing overall costs of operation.

A double-blind analysis of the output of our site-specific and geo-referenced computer models, compared to university research data, shows model output to be 85-93% accurate*. And where model output was used in large-scale field tests to time an early intervention strategy, crop yield increased by an average of twenty-two (22%)** (in a very dry year.)


* – Results of correlations between 2001 turfgrass disease model output and university control plot data

**- Results of 2001 large-scale field tests of Sclerotinia sps. control of canola in Saskatchewan, Canada


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