Organic vs. conventional grassland management: do (15)N and (13)C isotopic signatures of hay and soil samples differ?

Abstract

Distinguishing organic and conventional products is a major issue of food security and authenticity. Previous studies successfully used stable isotopes to separate organic and conventional products, but up to now, this approach was not tested for organic grassland hay and soil. Moreover, isotopic abundances could be a powerful tool to elucidate differences in ecosystem functioning and driving mechanisms of element cycling in organic and conventional management systems. Here, we studied the δ(15)N and δ(13)C isotopic composition of soil and hay samples of 21 organic and 34 conventional grasslands in two German regions. We also used Δδ(15)N (δ(15)N plant - δ(15)N soil) to characterize nitrogen dynamics. In order to detect temporal trends, isotopic abundances in organic grasslands were related to the time since certification. Furthermore, discriminant analysis was used to test whether the respective management type can be deduced from observed isotopic abundances. Isotopic analyses revealed no significant differences in δ(13)C in hay and δ(15)N in both soil and hay between management types, but showed that δ(13)C abundances were significantly lower in soil of organic compared to conventional grasslands. Δδ(15)N values implied that management types did not substantially differ in nitrogen cycling. Only δ(13)C in soil and hay showed significant negative relationships with the time since certification. Thus, our result suggest that organic grasslands suffered less from drought stress compared to conventional grasslands most likely due to a benefit of higher plant species richness, as previously shown by manipulative biodiversity experiments. Finally, it was possible to correctly classify about two third of the samples according to their management using isotopic abundances in soil and hay. However, as more than half of the organic samples were incorrectly classified, we infer that more research is needed to improve this approach before it can be efficiently used in practice.