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Candidatus Liberibacter solanacearum (CLso) is an unculturable bacterium vectored by the tomato potato psyllid (TPP) Bactericera cockerelli and has been associated with Zebra chip disease in potato and with other economically relevant symptoms observed in solanaceous crops. By altering their host and vector's biological system, pathogens are able to induce changes that benefit them by increasing their transmission rate. Understanding these changes can enable better targeting of mechanisms to control pathogen outbreaks. Here, we explored how the CLso infectious status affects the volatile organic compounds (VOCs) of the tomato plant, and whether the CLso infectious status of TPP influences host plant settlement. These chemical and behavioral changes can ultimately affect the rate of encounter between the host and the vector. Results from headspace volatile collection of tomato plants showed that CLso infected tomato plants emitted a qualitatively and quantitatively different blend of VOCs compared to sham-infected plants. By a factorial experiment, we showed that CLso negative (CLso-) TPP preferred to settle 70 % more often on infected tomato plants, while CLso positive (CLso+) TPP were found 68 % more often on sham-infected tomato plants. These results provide new evidence in favor of both host and vector manipulation by CLso.

Co-innovation can be effective for complex challenges-involving interactions amongst multiple stakeholders, viewpoints, perceptions, practices and interests across programmes, sectors and national systems. Approaches to challenges in the primary sector have tended to be linear, where tools and outputs are developed by a few, mostly scientists/researchers, and then extended to stakeholders. A co-innovation approach first deciphers and delineates the biophysical, societal, regulatory, policy, economic and environmental drivers, constraints and controls influencing these challenges at multiple levels. Second, stakeholder interactions and perspectives can inform and change the focus as well as help in co-developing solutions to deliver agreed outcomes. However, there is limited systematic and comparative research on how coinnovation works out in different projects. Here we analyse the results of applying a co-innovation approach to five research projects in the New Zealand primary sector. The projects varied in depth and breadth of stakeholder engagement, availability of ready-made solutions and prevalence of interests and conflicts. The projects show how and why co-innovation approaches in some cases contributed to a shared understanding of complex problems. Our results confirm the context specificity of co-innovation practices.

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Disease dynamics of Cercospora leaf spot (CLS) of sugar beet was analyzed at two hierarchical scales: as vertical profiles within individual plants and in relation to disease on neighboring plants. The relative contribution of different leaf layers to increase in CLS was analyzed using a simple continuous-time model. The model was fitted to data from two field trials in the Netherlands: one in an area with a long history of CLS, the other in an area where CLS has only recently established; in each case these were unsprayed and twice-sprayed treatments. There were differences in the relative contribution of different leaf layers to disease increase on the target leaf layer according to the CLS history and whether the plants were sprayed or unsprayed. In both field trials, parameter estimates giving the relative contribution of the target leaf layer to disease increase at that leaf layer were higher than those for the lower leaf layer. On only a few occasions the contribution of an upper leaf layer to disease increase at the target leaf layer was significant. Thus, CLS increase at the target leaf layer was determined mainly by disease severity at that leaf layer and to a lesser extent by disease at the lower leaf layer. Our continuous-time model was also used to analyze CLS increase on an individual sugar beet plant in relation to its own and its neighbor's level of disease in field trials at five locations in the two CLS areas over two years. In all field trials, the contribution of the target plant itself to disease increase (auto-infection) was larger than that of its neighboring plants (allo-infection). The overall analysis in the two CLS areas also indicated a larger contribution of the target plant to its disease increase than of neighboring plants, and this pattern was also apparent in a pooled analysis across all sites. Thus, CLS increase on a sugar beet plant was mainly determined by the disease severity on that plant and to a lesser extent by its within-row neighboring plants.