Abstract: Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.

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.