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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.

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The accuracy and precision of disease severity assessment data might be improved if there was a better understanding of how the laws of psychophysics actually relate to the theory and practice of phytopathometry. In this regard, we utilized a classical method developed in the field of psychophysics (the method of comparison stimuli) to test Horsfall and Barratt's claim that raters cannot accurately discriminate disease severity levels between 25% and 50% because, according to the Weber-Fechner law, visual acuity is proportional to the logarithm of the intensity of the stimulus. We show for two pathosystems, wheat leaf rust and grapevine downy mildew, that raters can accurately discriminate disease severity levels between 25% and 50%, and that although Weber's law appears to hold true, Fechner's law does not. Furthermore, based upon our results, the relationship between actual (true) disease severity (X) and disease severity estimated by raters (Y) is linear, not logarithmic as proposed by Horsfall and Barratt.