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Description of research cluster GRASSLAND SYSTEMS

Grassland, the largest agricultural biome on earth, contributes considerably to human nutrition through the conversion of roughage by ruminants into milk and meat. It also contributes to numerous ecosystem services that are emerging crucial in the 21st century. Due to economic pressure, sustainability and competitiveness of ruminant production systems is endangered. In some areas in the world, natural resources are being depleted and soil and vegetation being degraded to such an extent that grassland productivity itself declines. The development of balanced grassland production systems that are able to sustain land use in an ecologically friendly and economically competitive way remains a challenge to agricultural science and farm management.

 

The level at which future problems have to be solved is primarily the grassland farm. However, as key problems of grassland production are related to larger scales, upscaling from farm to region is needed. Accordingly, spatial information on production system and environment has to be partly relieved by remotely sensed data and its interpretation has to be supported by geographic information system. These technologies allow rapid detection of spatio-temporal variability of the grassland sward within farmland. They also support decisions that help improving the use-efficiency of natural resources such as soil fertility and water. Further, due to the complexity of the production systems, it is hardly possible assessing the impact of management changes and environmental conditions without simulation models.

 

  • Our research focuses on understanding how grassland management systems can be best adapted to potential productivity, environmental conditions and socio-economic needs.
  • We strive for identifying the local balance between level of production and required input on the one hand and environmental needs on the other.
  • We define factors by which vulnerability and resilience of grassland vegetation and soil fertility can be identified.
  • Our aim is to learn about the response of vegetation to environmental factors and management impact through expression of community plant functional traits, including its detection by non-destructive technology such as remote sensing. This should allow us explaining the functional relationships between type and intensity of management on the one hand and the response of grassland vegetation for a given environment on the other.
  • This also includes the identification of spatio-temporal variability of sward conditions by remote sensing aiming to obtain spatially explicit indictors of vulnerability.
  • Simulation models are applied to predict the impact of variation in management factors for a wider range of environmental conditions.

 


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