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This review considers the cascade of events that link injuries caused by plant pathogens on crop stands to possible (quantitative and qualitative) crop losses (damage), and to the resulting economic losses. To date, much research has focused on injury control to prevent this cascade of events from occurring. However, this cascade involves a complex succession of components and processes whereby knowledge on crop loss generates entry points for management. Proposed here is a framework linking different types of knowledge on crop loss to a range of decision categories, from tactical to strategic short- or long-term. Important advances in this field are now under way, including a probabilistic treatment of the injury-damage relationship, or analyses of the sources of uncertainty attached to some components of the decision process. Management of injury profiles, rather than individual injuries, and shifts in dimensionality of crop losses are anticipated to contribute to the design of sustainable agricultural systems, and address global issues concerning food security and food safety.

A series of experiments was conducted where a range of injuries due to rice pests (pathogens, insects, and weeds) was manipulated simultaneously with a range of production factors (fertilizer input, water supply, crop establishment method, variety) in different seasons and years. These factors were chosen to represent lowland rice production situations characterized in surveys conducted in tropical Asia and their corresponding range of attainable yield. Experiments complemented one another in exploring the response surface of rice yields to yield-limiting and yield-reducing factors. The resulting experimental data base consisted of 445 individual plots and involved 11 manipulated injuries in a range of attainable yields of 2 to 11 t ha-1. A first, nonparametric, multivariate analysis led to a hierarchy of potential injuries, from marginally (e.g., bacterial leaf blight) to extremely harmful (e.g., rice tungro disease). A second, parametric, multivariate approach resulted in a multiple regression model involving factors generated by principal component analysis on injuries that adequately described the variation in actual yield. One major finding was that some (attainable yield × injury factors) interactions significantly contributed to the description of variation in actual yield, indicating that some injuries (or their combinations) had a stronger or weaker yield-reducing effect, depending on the level of attainable yield. For instance, yield losses due to sheath blight, weed infestation, and rice tungro disease tend to increase, remain stable, and decrease, respectively, with increasing attainable yields. Back-computations using the principal component regression model estimated yield losses caused by individual injuries, using the mean injury levels in a population of farmers' fields surveyed across tropical Asia. The results indicate that sheath blight, brown spot, and leaf blast are diseases that cause important losses (between 1 and 10%) regionally. Among the insect injuries, only white heads caused by stem borers appear of relevance (2.3% yield losses). These injuries, however, do not match in importance those caused by weeds, whether outgrowing the rice crop canopy (WA) or not (WB), both types of injuries causing about 20% yield losses when considered individually. When all mean injuries were combined into one mean injury profile occurring at a regional attainable yield of 5.5 t ha-1, a mean yield loss of 37.2% was estimated, indicating that injuries were less than additive in their yield-reducing effects. Scenario analyses were conducted in a set of (production situations × injury profiles) combinations characterized from surveys in farmers' fields in tropical Asia. Depending on the scenario chosen, losses ranging from 24 to 41% were found.

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

The effects of crop management patterns on coffee rust epidemics, caused by Hemileia vastatrix , are not well documented despite large amounts of data acquired in the field on epidemics, and much modelling work done on this disease. One main reason for this gap between epidemiological knowledge and understanding for management resides in the lack of links between many studies and actual production situations in the field. Coffee rust epidemics are based on a seemingly simple infection cycle, but develop polycyclic epidemics in a season and polyetic epidemics over successive seasons. These higher-level processes involve a very large number of environmental variables and, as in any system involving a perennial crop, the physiology of the coffee crop and how it affects crop yield. Crop management is therefore expected to have large effects on coffee rust epidemics because of its immediate effect on the infection cycle, but also because of its cumulative effect on ongoing and successive epidemics. Quantitative examples taken from a survey conducted in Honduras illustrate how crop management, different combinations of shade, coffee tree density, fertilization and pruning may strongly influence coffee rust epidemics through effects on microclimate and plant physiology which, in turn, influence the life cycle of the fungus. We suggest there is a need for novel coffee rust management systems which fully integrate crop management patterns in order to manage the disease in a sustainable way.

The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host-pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.

Sheath blight (ShB) disease, caused by Rhizoctonia solani, is an economically important rice disease worldwide, especially in intensive production systems. Several studies have been conducted to identify sources for ShB resistance in different species of rice, including local accessions and landraces. To date, none of the genotypes screened are immune to ShB, although variation in levels of resistance have been reported. Several quantitative trait loci (QTL) for ShB resistance have been identified using mapping populations derived from indica or japonica rice. A total of 33 QTL associated with ShB resistance located on all 12 rice chromosomes have been reported, with ten of these colocalizing with QTL for morphological attributes, especially plant height, or for heading date. Sixteen QTL, from the same or differing genetic backgrounds, have been mapped at least twice. Of these, nine QTL were independent of morphological traits and heading date. We hypothesize that two main, distinct, mechanisms contribute to ShB resistance: physiological resistance and disease escape. Strategies to improve our understanding of the genetics of resistance to ShB are discussed.

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