Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding.

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We present discoursegraphs, a library and command-line application for the conversion and merging of linguistic annotations written in Python. The software reads and writes numerous formats for syntactic and discourse-related annotations, but also supports generic interchange formats. discoursegraphs models primary data and its annotations as a graph and is therefore able to merge multiple independent, possibly conflicting annotation layers into a unified representation. We show how this approach is beneficial for the revision and validation of a corpus with multiple conflicting, independently annotated layers.

I present rst-workbench, a software package that simplifies the installation and usage of numerous end-to-end Rhetorical Structure Theory (RST) parsers. 1 The tool offers a webbased interface that allows users to enter text and let multiple RST parsers generate analyses concurrently. The resulting RST trees can be compared visually, manually post-edited (in the browser) and stored for later usage.

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