Biomass handling operations (harvesting, raking, collection, and transportation) are critical operations within the agricultural production system since they constitute the first link in the biomass supply chain, a fact of substantial importance considering the increasingly involvement of biomass in bio-refinery and bio-energy procedures. Nevertheless, the inherent uncertainty, imposed by the interaction between environmental, biological, and machinery factors, makes the available scheduling and planning tools for field operations, inadequate to deal in a efficient way with  sequential tasks executed in an open and un-controlled environment. 

The main objectives of the project were to investigate the potential of selective biomass harvesting based on biomass moisture content.

A field experiment was conducted in the University farm of Aarhus University in Denmark in 2009. About 450 georeferenced biomass samples were collected when the forage was cut and left in the ground.  The samples were weighed and dried and then weighed again, thereby determining the water content in the specific sample.

For the prediction of the time-evolution of biomass moisture content as a function of the local weather forecast information a model based on Fluid Computational Dynamics (CFD), was developed. The use of a CFD model provides for the consideration of factors such as field topology, geometrical features and local weather conditions.    

Management zones were created for the specific field based on the initial biomass moisture content. The CFD model took into account, field topography and local weather conditions. Based on this model the data were produced for a 6-hour interval and the biomass moisture map were created. The results demonstrated specific management zones for the biomass moisture which could lead to selective biomass harvesting or collection. However, as the time was progressing the moisture was decreased and the variance was smaller leading to the fact that the heterogeneity of the biomass moisture was cancelling out. This may also lead to the development of a decision support tool that may assist the farmers for the best timing of collecting the biomass.

Further step on the system is the implementation of on-machine sensor systems for real-time measuring of the (cut) biomass quantity and moisture content, as the basis for the creation of biomass and moisture content maps depicting their spatial variability, as the basis for the development of a Decision Support System for the time-schedule of the execution of the harvesting, raking, and collecting field operations.