Systematic reviews and systematic maps represent powerful tools to identify, collect, evaluate and summarise primary research pertinent to a specific research question or topic in a highly standardised and reproducible manner. Even though they are seen as the "gold standard" when synthesising primary research, systematic reviews and maps are typically resource-intensive and complex activities. Thus, managing the conduct and reporting of such reviews can become a time consuming and challenging task. This paper introduces the open access online tool CADIMA, which was developed through a collaboration between the Julius Kühn-Institut and the Collaboration for Environmental Evidence, in order to increase the efficiency of the evidence synthesis process and facilitate reporting of all activities to maximise methodological rigour. Furthermore, we analyse how CADIMA compares with other available tools by providing a comprehensive summary of existing software designed for the purposes of systematic review management. We show that CADIMA is the only available open access tool that is designed to: (1) assist throughout the systematic review/map process; (2) be suited to reviews broader than medical sciences; (3) allow for offline data extraction; and, (4) support working as a review team.

AbstractmOver a period of 3 years, the essential volatile compounds of several strawberry varieties were analysed by gas chromatography, gas chromatography-olfactometry and mass spectrometry. In general, a strong variability in the dependence of the amount of these compounds on the ripening stage, climate and location was found, nevertheless, the key compounds of the aroma showed typical, genetically determined basic patterns. The quantification of the key aroma compounds in cultivated and wild strawberries resulted in a definition of aroma types which corresponded with the sensory evaluation. These aroma types can be used to establish a criterion for the selection of quality in strawberry breeding.

The strawberry, with its unique aroma, is one of the most popular fruits worldwide. The demand for specific knowledge of metabolism in strawberries is increasing. This knowledge is applicable for genetic studies, plant breeding, resistance research, nutritional science, and the processing industry. The molecular basis of strawberry aroma has been studied for more than 80 years. Thus far, hundreds of volatile organic compounds (VOC) have been identified. The qualitative composition of the strawberry volatilome remains controversial though considerable progress has been made during the past several decades. Between 1997 and 2016, 25 significant analytical studies were published. Qualitative VOC data were harmonized and digitized. In total, 979 VOC were identified, 590 of which were found since 1997. However, 659 VOC (67%) were only listed once (single entries). Interestingly, none of the identified compounds were consistently reported in all of the studies analyzed. The present need of data exchange between "omic" technologies requires high quality and robust metabolic data. Such data are unavailable for the strawberry volatilome thus far. This review discusses the divergence of published data regarding both the biological material and the analytical methods. The VOC extraction method is an essential step that restricts interlaboratory comparability. Finally, standardization of sample preparation and data documentation are suggested to improve consistency for VOC quantification and measurement.

Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale.

Priming allows plants to respond faster and stronger to abiotic or biotic stresses. Leaf rust (Puccinia hordei) is an important pathogen of barley (Hordeum vulgare), for which resistance genes are known, but mostly overcome. Therefore, the aims of this study were (i) to establish a priming system in barley, based on bacterial N-acyl homoserine lactone (AHL), and (ii) to get information on the effect of priming on the reaction to leaf rust. Plants were inoculated with bacteria, i.e., Ensifer meliloti with repaired expR copy, producing the oxo-C14-homoserine lactone (AHL) and an E. meliloti strain carrying the attM lactonase gene from Agrobacterium tumefaciens, which cleaves the AHL and acts here as negative control. After three bacterial inoculations, plants were challenged with P. hordei strain I-80 at the three leaves stage. Twelve days after infection, scoring of the leaf area diseased and the infection type was conducted followed by the calculation of the relative susceptibility. First results indicate a significantly (P < 0.001) higher resistance level to P. hordei after inoculation with E. meliloti. Furthermore, significant (P < 0.001) differences were detected between the accessions tested for priming efficiency, which can be the basis to screen a larger set of barley accessions to detect quantitative trait loci or candidate genes involved in priming. [Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .