SIGNAL

Motivation

The joint project SIGNAL as part of the funding program BonaRes (Soil as a Sustainable Resource for the Bioeconomy) investigates over a period of up to 9 years the effects of agroforestry systems on the biological functions of the soil, the rhizosphere, the aboveground material fluxes as well as the water use efficiency of the soils. The basis of the research approaches within the project is the central hypothesis that innovative land use systems consisting of coupled cultivation of trees or shrubs with cropland or grassland (agroforestry systems) can have positive ecological, economic, and aesthetic effects in contrast to conventional crop production systems.

Agroforestry systems can achieve higher total area yields compared to growing the crops separately as a monoculture. At the same time, as a result of interactions between tree and arable/grassland crops, yield differences occur within the arable/grassland strips, which can vary significantly depending on the system and location. The specific expression of spatial variability is very significant for the ecological and economic evaluation of the agroforestry system. So far, the measurement of yields has often been based on point surveys in transects or recording with the (plot) combine. The variability of crop parameters with distance to trees can only be represented very imprecisely with these methods. High spatial resolution and coverage can only be achieved with very high effort.

Aims and approach

The research activities of the GNR department in the second phase of the SIGNAL project therefore aim at the development of remote sensing methods that can map the small-scale variability of yield-relevant crop production parameters at high resolution and over large areas. To this end, drone-based remote sensing will be used to collect high-resolution multispectral and hyperspectral data in the conventionally managed silvopastoral (Mariensee, Reiffenhausen) and silvoarable agroforestry trials (Wendhausen, Dornburg, Forst) of the SIGNAL project. In addition, point clouds will be created using images from a commercial photographic drone (Structure From Motion) to estimate biomass yields via plant height. The aim is to find out which sensors are best suited for large-scale yield estimation and which spatial patterns can be found in the investigated agroforestry systems.

The goal of this work is both to test a simple method for large-scale, high-resolution collection of yield-related crop parameters within agroforestry systems, and to better understand the ecological interactions between tree strips and agricultural crops.

Innovations and perspectives

The scope of the GNR department of the University of Kassel/Witzenhausen in the second phase of the collaborative project is the evaluation of methods to assess the spatial variability of biomass and grain yields in agroforestry systems.

Another work area of the project is the continuation of the long-term data collection in the silvopastoral agroforestry system of the Universities of Kassel and Göttingen in Reiffenhausen with regard to the biomass development of grassland and pasture stands and the determination of various quality parameters.