Abstract

With a fast changing climate and the rise of human population there is an increasing concern on food security whilst avoiding environmental damage. The beneficial effects of crop rotation and enhanced sown agro-diversity on yield production and ecosystem functioning have been demonstrated, although their persistence over time and our ability to detect such lasting legacy effects on the following crops have been barely investigated, thus hindering their adoption as an economically sustainable and ecologically sound solution. Here, we address this issue with a biodiversity-ecosystem functioning experiment in an agricultural field where we manipulated the composition and relative proportion of three forage species crossed with two levels of nitrogen fertilization from 2008 to 2011. Two years after the end of the experiment we investigated the potential legacy effects of previous sown diversity over the performance of the following crop and assessed whether these effects are detectable with remote sensing tools mounted in unmanned aerial vehicles (UAV). Previous plant diversity had a strong legacy effect influencing the biomass production of the next cereal crop. For instance, on average a forb-grass mixture increased two times more the leaf biomass production and the leaf area index of the following wheat crop than a legume monoculture. Thus, diversity-interaction models showed that species mixtures had greater legacy effects over crop performance than monocultures, while experimental Nitrogen fertilization did not show legacy effects. The influence of previous plant diversity on the performance of the following crop was detectable by means of a remote sensing index like the Green-Red Vegetation Index. Biodiversity-ecosystem function relationships may result in lasting legacy effects leading to enhanced crop performance with increasing agrodiversity. Lower amounts of fertilizer may be needed by increasing the sown diversity during crop rotation, whose effects can be easily scaled-up and monitored by UAV and remote sensing tools.

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