Differential physiological responses of different rice (Oryza sativa) cultivars to elevated night temperature during vegetative growth.

Global climate change is leading to asymmetric atmospheric warming with reduced temperature differences between day and night. This has an increasing influence on crop plants. However, little is known about the physiology of high night temperature (HNT) effects and natural variation in HNT susceptibility. Twelve rice cultivars were investigated under HNT (30°C day/28°C night) and control (28°C day/21°C night) conditions. Chlorosis was observed under HNT and used to classify relative sensitivity of cultivars. The resulting mean sensitivity rank correlated significantly with seed yield under HNT (r=-0.547). Wide variability in HNT tolerance led to an increase in shoot FW and DW in tolerant, but decreased plant growth in sensitive cultivars. Growth parameters correlated negatively with HNT sensitivity. Respiration rate was significantly increased under HNT at the end of night for several cultivars 34 DAS and 41 DAS and for all cultivars 66 DAS whereas photosynthetic quantum yield was not influenced. Negative correlations of sensitivity rank with respiration rate at two time points under HNT (r=-0.305; r=-0.265) exclude higher respiration rates in sensitive cultivars as a primary cause for HNT sensitivity. Monosaccharide and starch concentrations of leaves were increased after 16 days of HNT, while sucrose was not affected. Additionally tolerant cultivars showed a higher increase of monosaccharide concentrations during the day under HNT compared with control conditions. While HNT did not lead to carbon depletion in rice leaves, tolerant cultivars coped better with HNT, enabling them to accumulate more carbohydrates than sensitive cultivars with leaves affected by chlorosis.

Data management pipeline for plant phenotyping in a multisite project.

In plant breeding, plants have to be characterised precisely, consistently and rapidly by different people at several field sites within defined time spans. For a meaningful data evaluation and statistical analysis, standardised data storage is required. Data access must be provided on a long-term basis and be independent of organisational barriers without endangering data integrity or intellectual property rights. We discuss the associated technical challenges and demonstrate adequate solutions exemplified in a data management pipeline for a project to identify markers for drought tolerance in potato. This project involves 11 groups from academia and breeding companies, 11 sites and four analytical platforms. Our data warehouse concept combines central data storage in databases and a file server and integrates existing and specialised database solutions for particular data types with new, project-specific databases. The strict use of controlled vocabularies and the application of web-access technologies proved vital to the successful data exchange between diverse institutes and data management concepts and infrastructures. By presenting our data management system and making the software available, we aim to support related phenotyping projects.