Abstract: A data set generated by a multi-year (2003–2010) and multi-site network of experiments on winter wheat varieties grown at different levels of crop management is analysed in order to assess the importance of climate on the variability of wheat health. Wheat health is represented by the multiple pathosystem involving five components: leaf rust, yellow rust, fusarium head blight, powdery mildew, and septoria tritici blotch. An overall framework of associations between multiple diseases and climate variables is developed. This framework involves disease levels in a binary form (i.e. epidemic vs. non-epidemic) and synthesis variables accounting for climate over spring and early summer. The multiple disease-climate pattern of associations of this framework conforms to disease-specific knowledge of climate effects on the components of the pathosystem. It also concurs with a (climate-based) risk factor approach to wheat diseases. This report emphasizes the value of large scale data in crop health assessment and the usefulness of a risk factor approach for both tactical and strategic decisions for crop health management.

Abstract: Highlights
• 14 criteria for use of crop models in assessments of impacts, adaptation and risk • Working with stakeholders to identify timing of risks is key to risk assessments. • Multiple methods needed to critically assess the use of climate model output • Increasing transparency and inter-comparability needed in risk assessments
Abstract
Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

Abstract: Agricultural vulnerability to climate change is likely to vary considerably between agro-environmental regions. Exemplified on Austrian cropland, we aim at (i) quantifying climate change impacts on agricultural vulnerability which is approximated by the indicators crop yields and gross margins, (ii) developing robust crop production portfolios for adaptation, and (iii) analyzing the effect of agricultural policies and risk aversion on the choice of crop production portfolios. We have employed a spatially explicit, integrated framework to assess agricultural vulnerability and adaptation. It combines a statistical climate change model for Austria and the period 2010-2040, a crop rotation model, the bio-physical process model EPIC (Environmental Policy Integrated Climate), and a portfolio optimization model. We find that under climate change, crop production portfolios include higher shares of intensive crop management practices, increasing average crop yields by 2-15% and expected gross margins by 3-18%, respectively. The results depend on the choice of adaptation measures and on the level of risk aversion and vary by region. In the semi-arid eastern parts of Austria, average dry matter crop yields are lower but gross margins are higher than in western Austria due to bio-physical and agronomic heterogeneities. An abolishment of decoupled farm payments and a threefold increase in agri-environmental premiums would reduce nitrogen inputs by 23-33%, but also crop yields and gross margins by 18-37%, on average. From a policy perspective, a twofold increase in agri-environmental premiums could effectively reduce the trade-offs between crop production and environmental impacts. (C) 2015 Elsevier Ltd. All rights reserved.