AWhite Paper on Global Wheat Health Based on Scenario Development and Analysis

Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems that are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with Fusarium head blight; biotrophic fungi, Septoria-like fungi, necrotrophic fungi, soilborne pathogens, and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology. Plant disease epidemiology generates very diverse information linking the dynamics of plant diseaseswith the physical environment, the genetic variation and variability of pathogens, the genetic makeup and diversity of plant populations, the organization of landscapes, or the management of plant populations as a result of decisions of different types. Many of the important questions faced by plant disease epidemiology delve on the consideration of multiple processes, involving several epidemiological components, which are under the influence of several factors. Given the complexity of the systems which epidemiological analyses address, a range of advanced statistical and modeling techniques are in routine use. Among the approaches to synthesize information, notable for instance are the methods of statistical meta-analysis, which enable to assemble multiple data sets pertaining to the same question, and derive overall conclusions (Paul et al. 2005). Simulation modeling constitutes another type of approach, which may be considered when knowledge on processes, along with quantitative measurements, are available to both parameterize and evaluate a model that sums-up key hypotheses on the functioning of the considered disease system (Rabbinge and De Wit 1989; Savary et al. 2006). On the other hand, when the system under consideration is very complex and diverse, when a large fraction of the information is qualitative, and when future projections involve many uncertainties, scenario analysis (Öborn et al. 2013; Sundström et al. 2014) constitutes an interesting approach to synthesize knowledge, identify knowledge gaps, and derive hypotheses. Scenario analyses allow projection of dynamic processes under a range of combined hypotheses (collectively called “scenarios”). For example, the Millennium Ecosystem Assessment conducts a global analysis along four scenarios, which were set according to (i) global development paths (globalization and regionalization), and (ii) approaches to ecosystem management (reactive and proactive policies; Millennium Ecosystem Assessment 2005). More recently, the Agrimonde analysis (Paillard et al. 2014) involved two contrasting scenarios, addressing agriculture and food security in 2050: Agrimonde 1, a scenario of sustainable food and agricultural development, andAgrimondeGO, a scenario of global markets and major technological progress. Scenario analyses are also key components in projects such as the Agriculture Modeling Intercomparison Project (AGMiP), whereby scenarios are developed according to both climate change and socio-economic considerations (Rosenzweig and Hillel 2015, page 970). Scenario analysis was introduced in the field of plant health Corresponding author: S. Savary; E-mail: serge.savary@toulouse.inra. fr © 2017 The American Phytopathological Society Vol. 107, No. 10, 2017 1109 Phytopathology • 2017 • 107:1109-1122 • https://doi.org/10.1094/PHYTO-01-17-0027-FI