AGRITECHNICA 2017 | Claas and Kemper take Agritechnica Gold

Kemper has won an Agritechnica innovation gold medal for its StalkBuster – the first stubble-destroying technology that forms an integral part of a maize header

A second innovation gold medal was awarded to Claas for its Cemos Auto Threshing system

Developed jointly with John Deere, the StalkBuster is the only machine on the market that destroys all the stubble before it is driven on by the forager or the tractor and trailer.

Usually about 30 per cent of the stubble remains intact and, as the corn borer winters inside it, a high percentage of undestroyed stubble offers them a haven so they can infest the area again the following year.

The new topper is integrated in the header, is relatively lightweight, and has a relatively low power input requirement. This means no extra limitations apply for legal road transport.

The Kemper StalkBuster is a technical solution that offers great benefits for users, their productivity, and the environment.

A second innovation gold medal was awarded to Claas for its Cemos Auto Threshing system, and a further 29 silver medals were awarded to a host of different entries from smart technology to practical engineering.

Cemos Auto Threshing is the first system that sets the tangential threshing system on straw walker and hybrid machines automatically.

Depending on the strategy entered into the system by the operator, it sets the drum speed and the concave gap for optimum results in the current harvest conditions.

The USP of the entire system is the fact that all controllers communicate with each other. For example, the throughput controller operates via a special communication module to control the throughput relative to the threshing controller, as well as the separation and cleaning controllers.

Another module in the system is auto threshing, which, for the first time, completes the enormous complex technical step to implement fully automated threshing.

On such a harvester, users no longer need to know which settings they have to make to get the desired results. Instead, they enter the harvesting strategy, which is then used by the auto-learning system to optimize all parameters.