Optimization of field traffic to ensure soil protection and efficiency: the case of grass harvest

Soil compaction is a major threat to global agriculture as it negatively affects both plant production and has detrimental impacts on the environment. Compaction in the subsoil is persistent for decades. In developed and industrialized countries, farmers are forced to make any part of the production as efficient and cost-effective as possible, which has led to the construction of very large machinery for field operations, and traffic in the field in non-optimal conditions (when the soils have a low bearing capacity). The trafficked area can be reduced by adopting control traffic farming (CTF). However, CTF means the use of much bigger machinery, and heavily compacted traffic lines will expand with permanent detrimental consequences on soil functions. Then, sustainable traffic can be only achieved by limiting the trafficked area AND by adjusting the vehicle capacity to soil conditions. Focus of today’s technology development is on the ability of a vehicle to traverse a terrain to perform a given operation and not on soil protection. Technologies that could help to avoid the risk of soil compaction exist already: wide tires with low inflation pressure, central tire inflation adjustment systems, GPS steering to reduce trafficked area, traction distribution on several axles. A decision support tool (DSS) is now needed to make full use of these highly relevant technological advances.

The aim of the SOLGRAS project is to support farmers and contractors for planning traffic in the field during harvest operations that will allow both optimization of overall resource utilization and mitigation of soil compaction, and thereby improving crop productivity, reducing energy use, and improving profits from primary plant production. The overall objective is to propose a decision support system (DSS) for optimizing traffic during grass harvest operations that will make full use of available topography, soil, climate, and machinery data at the field scale.

The project is funded by GUDP.