Vassilis Protonotarios

Leaf Responses of Young Iron‐Inefficient Maize Plants to Sulfur Deprivation

Abstract Hydroponically‐grown young iron (Fe)‐inefficient maize (Zea mays L.) plants were deprived of the external source of sulfate following an initial period when the sulfur (S)‐supply was sufficient. The effects of sulfate deprivation on leaf dry weight, dry weight to fresh weight ratio, chlorophyll fluorescence, SPAD reading, nitrogen (N) concentration, and Fe concentration of the lower leaves were monitored for 10 days. The patterns of leaf mass, area, chlorophyll content, and SPAD readings were analyzed according to leaf position. Decreased Fe concentration of leaf tissues was observed in all plants after the fourth day of the experiment, suggesting that this cultivar was Fe‐inefficient. An initial effect of short‐term S‐deprivation on leaves of young Fe‐inefficient maize plants was a lower Fe concentration of lower leaves in the second day. The fourth day of S‐deprivation experiment was a critical stage for the lower leaves, characterized by exhaustion of internal sulfate pools. After day 4, S‐deprivation affected both Fe‐ and N‐concentration of lower leaves. Nitrogen‐concentration remained stable and significantly less than that of the control plants and chlorosis became apparent. From the sixth day onwards, the lower leaves were characterized by decreased dry mass, higher dry weight to fresh weight ratio indicating less water content, less chlorophyll content although existing PSII systems were not affected, lower Fe concentration, and lower N concentration. Leaf development ceased, the fifth leaf did not emerge and the fourth one was less developed, the leaf mass to area ratio of the first three leaves was lowered, a progressive delay in the pattern of partitioning of chlorophyll content among leaves was observed, and the distribution of chlorosis intensity within the leaf blade was altered. Thus, after the sixth day the S‐starved plants experienced a complex constraint consisting of S‐depletion, Fe‐deficiency, and induced N‐deficiency.

Developing data interoperability using standards: A wheat community use case

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach’s potential to be generalizable to other (agricultural) domains.

agINFRA: a research data hub for agriculture, food and the environment.

The agINFRA project (www.aginfra.eu) was a European Commission funded project under the 7th Framework Programme that aimed to introduce agricultural scientific communities to the vision of open and participatory data-intensive science. agINFRA has now evolved into the European hub for data-powered research on agriculture, food and the environment, serving the research community through multiple roles. Working on enhancing the interoperability between heterogeneous data sources, the agINFRA project has left a set of grid- and cloud- based services that can be reused by future initiatives and adopted by existing ones, in order to facilitate the dissemination of agricultural research, educational and other types of data. On top of that, agINFRA provided a set of domain-specific recommendations for the publication of agri-food research outcomes. This paper discusses the concept of the agINFRA project and presents its major outcomes, as adopted by existing initiatives activated in the context of agricultural research and education.