Free Access to Omnia’s new Climate Tool – Nick Strelczuk, David Howard

The Omnia Climate module provides more detailed and accurate weather data than is possible from physical weather stations, which are notoriously expensive costing anything from £800-£1000, and need constant maintenance, he explains.

“For this reason it’s never been feasible to have weather stations dotted around the farm, nor is it an option to drive around different units collecting data whenever a spray timing is looming. Subsequently crop management decisions based on weather information are often inaccurate.”

The concept behind the Climate module comes from the Predict & Justify project at Hutchinsons national Helix farm in Northamptonshire, explains David Howard, head of integrated crop management at Hutchinsons.

“The premise of the project is to look at how data from the field can be turned into agronomical knowledge, so this means looking beyond data collection, and taking it a step further into interpreting the data to develop better and more accurate modelling to support better informed decision making on farm.”

“The Climate module brings together weather modelling and monitoring data and combines it with Artificial Intelligence (AI) and machine learning. This has allowed us to develop models which help to improve risk prediction on farm.”

“Improving the accuracy of crop modelling lends itself to pro-active decision making and more accurate agronomic advice which is the cornerstone of integrated crop management,” explains David.

Based on this, Hutchinsons has developed three prediction models within the Climate module: crop growth, pest, disease and blight prediction modelling.

However like all of the tools within Omnia, the user can override the system, so if for example a growth stage is delayed, it can be reconfigured manually, he points out.

“It’s important that the model reflects what’s in the ground for it to be useful and valuable.”

Future plans will aim to utilise the AI within the model alongside layers of data already within Omnia to make modelling even more bespoke and accurate. For example taking into account varietal differences, seed rate, location, soil type, and inputs.

Barley Yellow Dwarf Virus (BYDV) is becoming an ever increasing threat to crop production following the loss of neonicotinoids.

“Growers need to balance the limited control options against aphid pressure – so knowing exactly when is the right time to spray for optimum control – is vital.”

The Pest and Disease forecast model aims to tackle this by using weather data to make treatment timings as accurate as they can be – on a field by field basis.

“The risk is presented in a graph, and accrues degree days along the 170 degree-day model that’s frequently used. The graph counts up time and as it reaches 150-day degrees, it shows up as a warning period to alert growers when they’re reaching a key timing.”

“This email alert can be set by the user at a time dictated by themselves, say 3 days before for example.”

“The model tracks when crops are drilled, as with every different drilling date there is a different risk period. For example season, wheat was drilled in November through to the end of February and is at different levels of risk accordingly.”

As with the crop growth model, growers and agronomists can manually reconfigure the model, and treatments –plus treatment dates – can be added to reset the model, he adds.

“This data can also be collated into a report to illustrate the levels of risk across the whole season and how they were treated, which will be very useful when it comes to making decisions in the future.”

Potato growers usually have crops spread over a wide area so using data from one weather station to predict blight events is fairly inaccurate,” says David. “However using the blight monitoring and predication model, it is possible to access precise weather data for a specific location for 10 days.”

“This makes understanding the exact risk much more accurate and is particularly useful in tight seasons to make sure that sprays are going on at the right place at the right time.”

Using a timeline, it is also possible for users to see what the risk is across a whole season, and then this this can be compared with treatment programmes to ensure crops have protection right the way through the season, he explains.

“At times when growers are not able to get onto land to travel, they can revert back to this data which will allow them to make decisions about which fields are at the highest risk and treat these first when able to travel again.”

The Climate module can be used for a wide range of agronomic decisions, that help to substantiate and evidence agronomic practices, points out David.

“In its simplest form, this could be spray forecasting and knowing when to access land. Or when drilling OSR; crops that establish faster and get up and away have a better chance of beating CSFB damage, so using the 10 day forecast and soil moisture conditions will help to ensure drilling happens at the right time. The same principle could be used for applying residual herbicides, as these need moisture to work effectively.”

“A real life example from the Helix farm last year played out when we had low rainfall during the peak growing season and a decision had to be made on reducing or cutting out the final nitrogen dressing.”

“However, the weather data in the Climate module showed that rain was predicted and that particular fields had adequate moisture to achieve yield potential- and infact this crop yielded 10t/ha, which wouldn’t have been possible without the additional nitrogen.”