We ask how crop diversification at different spatial scales affects the multifunctional response of agroecosystems in terms of crop growth, yield, input reduction, resource use and use efficiency and biodiversity. We focus on effects of crop mixtures and of varying field size or field geometry on multifunctional agroecosystem performance.
Crop mixtures such as mixes of cereals and pulses offer multiple advantages over sole crops, including more efficient resource use, reduction of production risk, reduction of pests and diseases, improved weed suppression, and increased biodiversity and higher plant diversity. Currently, however, crop mixtures are not widely adopted by farmers, for technical, socio-cultural, economic and agronomic reasons. In many cases, the complex knowledge required to successfully grow crop mixtures is not sufficiently present. Therefore, this project aims to develop advanced sensing, modelling and machine learning tools to select optimal partners in crop mixtures and to gain insights into the mechanisms governing the multifunctional performance of mixtures at the agroecosystem level. For this we select multiple partners to cover a large space of trait diversity, based on available data to get a set of crops for experimental testing. Then we test a large number of binary mixtures and sole crops in multiple environments and characterize the test environments via soil mapping and phenotype many mixtures in multiple environments. This includes measuring multiple parameters of crop growth with focus on competition between partners and crop yield. Ultimately we aim to generate promising mixtures with respect to yield, yield stability and other ecosystem services.
The development of new light-weight robotic field technology offers the possibility to considerably decrease field sizes, and to reshape field geometries, because the new machinery can potentially deal with variable sizes and shapes and does not require large rectangular fields. Crop management can therefore be adapted to prevailing spatial heterogeneity of the environment, in particular of the soil, thereby improving resource use efficiency and enhancing spatial crop diversity in the field. Spatially adapted management will focus on the choice of crop species and crop sequences as a function of soil heterogeneity. Currently it is unknown how these envisaged changes will affect the agroecosystem performance in terms of competition and neighbor effects, resource use efficiencies and biotic interactions, e.g. regarding plant disease epidemiology or biodiversity effects. To gather new insights in these areas, we set up a large field experiment with varied field geometries.