In this paper, biochemical process equations are presented as a basis for water quality modelling in rivers under aerobic and anoxic conditions. These equations are not new, but they summarise parts of the development over the past 75 years. The primary goals of the presentation are to stimulate communication among modellers and field-oriented researchers of river water quality and of wastewater treatment, to facilitate practical application of river water quality modelling, and to encourage the use of elemental mass balances for the derivation of stoichiometric coefficients of biochemical transformation processes. This paper is part of a series of three papers. In the first paper, the general modelling approach is described; in the present paper, the biochemical process equations of a complex model are presented; and in the third paper, recommendations are given for the selection of a reasonable submodel for a specific application.

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Sugar beet (Beta vulgaris) is an important crop plant that accounts for 30% of the world's sugar production annually. The genus Beta is a distant relative of currently sequenced taxa within the core eudicotyledons; the genomic characterization of sugar beet is essential to make its genome accessible to molecular dissection. Here, we present comprehensive genomic information in genetic and physical maps that cover all nine chromosomes. Based on this information we identified the proposed ancestral linkage groups of rosids and asterids within the sugar beet genome. We generated an extended genetic map that comprises 1127 single nucleotide polymorphism markers prepared from expressed sequence tags and bacterial artificial chromosome (BAC) end sequences. To construct a genome-wide physical map, we hybridized gene-derived oligomer probes against two BAC libraries with 9.5-fold cumulative coverage of the 758 Mbp genome. More than 2500 probes and clones were integrated both in genetic maps and the physical data. The final physical map encompasses 535 chromosomally anchored contigs that contains 8361 probes and 22 815 BAC clones. By using the gene order established with the physical map, we detected regions of synteny between sugar beet (order Caryophyllales) and rosid species that involves 1400-2700 genes in the sequenced genomes of Arabidopsis, poplar, grapevine, and cacao. The data suggest that Caryophyllales share the palaeohexaploid ancestor proposed for rosids and asterids. Taken together, we here provide extensive molecular resources for sugar beet and enable future high-resolution trait mapping, gene identification, and cross-referencing to regions sequenced in other plant species.

Rapid identification of agronomically important genes is of pivotal interest for crop breeding. One source of such genes are crop wild relative (CWR) populations. Here we used a CWR population of <200 wild beets (B. vulgaris ssp. maritima), sampled in their natural habitat, to identify the sugar beet (Beta vulgaris ssp. vulgaris) resistance gene Rz2 with a modified version of mapping-by-sequencing (MBS). For that, we generated a draft genome sequence of the wild beet. Our results show the importance of preserving CWR in situ and demonstrate the great potential of CWR for rapid discovery of causal genes relevant for crop improvement. The candidate gene for Rz2 was identified by MBS and subsequently corroborated via RNA interference (RNAi). Rz2 encodes a CC-NB-LRR protein. Access to the DNA sequence of Rz2 opens the path to improvement of resistance towards rhizomania not only by marker-assisted breeding but also by genome editing.

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.