UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.

The Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB). Manual annotations provided by UniProt curators are supplemented by manual and automatic annotations from model organism databases and specialist annotation groups. GOA currently supplies 368 million GO annotations to almost 54 million proteins in more than 480 000 taxonomic groups. The resource now provides annotations to five times the number of proteins it did 4 years ago. As a member of the GO Consortium, we adhere to the most up-to-date Consortium-agreed annotation guidelines via the use of quality control checks that ensures that the GOA resource supplies high-quality functional information to proteins from a wide range of species. Annotations from GOA are freely available and are accessible through a powerful web browser as well as a variety of annotation file formats.

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

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The Wisconsin Phosphorus Index (WPI) is one of several P indices in the United States that use equations to describe actual P loss processes. Although for nutrient management planning the WPI is reported as a dimensionless whole number, it is calculated as average annual dissolved P (DP) and particulate P (PP) mass delivered per unit area. The WPI calculations use soil P concentration, applied manure and fertilizer P, and estimates of average annual erosion and average annual runoff. We compared WPI estimated P losses to annual P loads measured in surface runoff from 86 field-years on crop fields and pastures. As the erosion and runoff generated by the weather in the monitoring years varied substantially from the average annual estimates used in the WPI, the WPI and measured loads were not well correlated. However, when measured runoff and erosion were used in the WPI field loss calculations, the WPI accurately estimated annual total P loads with a Nash-Sutcliffe Model Efficiency (NSE) of 0.87. The DP loss estimates were not as close to measured values (NSE = 0.40) as the PP loss estimates (NSE = 0.89). Some errors in estimating DP losses may be unavoidable due to uncertainties in estimating on-farm manure P application rates. The WPI is sensitive to field management that affects its erosion and runoff estimates. Provided that the WPI methods for estimating average annual erosion and runoff are accurately reflecting the effects of management, the WPI is an accurate field-level assessment tool for managing runoff P losses.

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