Addressing the threat of climate change to agriculture requires improving crop resilience to short-term abiotic stress

Climate change represents a serious threat to global agriculture, necessitating the development of more environmentally resilient crops to safeguard the future of food production. The effects of climate change are appearing to include a higher frequency of extreme weather events and increased day-to-day weather variability. As such, crops which are able to cope with short-term environmental stress, in addition to those that are tolerant to longer term stress conditions are required . It is becoming apparent that the hitherto relatively little studied process of post-stress plant recovery could be key to optimizing growth and production under fluctuating conditions with intermittent transient stress events. Developing more durable crops requires the provision of genetic resources to identify useful traits through the development of screening protocols. Such traits can then become the objective of crop breeding programmes. In this study, we discuss these issues and outline example research in leafy vegetables that is investigating resilience to short-term abiotic stress

Assembly and characterisation of a unique onion diversity set identifies resistance to Fusarium basal rot and improved seedling vigour

Conserving biodiversity is critical for safeguarding future crop production. Onion (Allium cepa L.) is a globally important crop with a very large (16 Gb per 1C) genome which has not been sequenced. While onions are self-fertile, they suffer from severe inbreeding depression and as such are highly heterozygous as a result of out-crossing. Bulb formation is driven by daylength, and accessions are adapted to the local photoperiod. Onion seed is often directly sown in the field, and hence seedling establishment is a critical trait for production. Furthermore, onion yield losses regularly occur worldwide due to Fusarium basal rot caused by Fusarium oxysporum f. sp. cepae. A globally relevant onion diversity set, consisting of 10 half-sib families for each of 95 accessions, was assembled and genotyping carried out using 892 SNP markers. A moderate level of heterozygosity (30–35%) was observed, reflecting the outbreeding nature of the crop. Using inferred phylogenies, population structure and principal component analyses, most accessions grouped according to local daylength. A high level of intra-accession diversity was observed, but this was less than inter-accession diversity. Accessions with strong basal rot resistance and increased seedling vigour were identified along with associated markers, confirming the utility of the diversity set for discovering beneficial traits. The onion diversity set and associated trait data therefore provide a valuable resource for future germplasm selection and onion breeding

Quasi-Bayesian Analysis Using Imprecise Probability Assessments And The Generalized Bayes’ Rule

The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior beliefs are represented by imprecise probability models. We describe a procedure for deriving coherent imprecise probability models when the event space consists of a finite set of mutually exclusive and exhaustive events. The procedure is based on Walley’s theory of upper and lower prevision and employs simple linear programming models. We then describe how these models can be updated using Cozman’s linear programming formulation of the GBR. Examples are provided to demonstrate how the GBR can be applied in practice. These examples also illustrate the effects of prior imprecision and prior-data conflict on the precision of the posterior probability distribution. Copyright Springer 2005imprecise probability, generalized Bayes’ rule, second-order probability, quasi-Bayesian analysis,

Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification

www.idsia.ch Abstract. Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions can provide a more realistic description in these cases. Bayesian networks can be generalized to cope with sets of distributions. This leads to a novel class of imprecise probabilistic graphical models, called credal networks. In particular, classifiers based on Bayesian networks are generalized to so-called credal classifiers. Unlike Bayesian classifiers, which always detect a single class as the one maximizing the posterior class probability, a credal classifier may eventually be unable to discriminate a single class. In other words, if the available information is not sufficient, credal classifiers allow for indecision between two or more classes, this providing a less informative but more robust conclusion than Bayesian classifiers