1ADAS Boxworth, Boxworth, Cambridge CB3 8NN, UK; 2ADAS High Mowthorpe, Duggleby, Malton, N. Yorks YO17 8BP, UK; 3University of Nottingham, Sutton Bonington Campus, Loughborough, Leics LE12 5RD, UK

Background and objectives
There are two serious difficulties in reconciling research on disease prediction with research on yield prediction. The first concerns the framework used to explain disease effects, and the second concerns the units used to measure disease. Considering the framework first, disease predictions are made according to concepts of host resistance and pathogen pressure. At present these concepts are considered subjectively as influencing a perceived, but unquantified, threat of disease progression. Although it is clear that resistance reduces disease progress and pressure increases disease progress, there is no way of working out, for instance, the degree of resistance necessary to nullify a certain amount of pressure. Disease control decisions generally concern the application of fungicides in order to dispel a perceived threat, by supplementing resistance. At present there is considerable subjectivity in setting a dose of fungicide to confer the requisite additional resistance, and there is much current research which aspires to optimise doses of fungicides, so that no more than the appropriate amount of chemical needs to be applied. Such research cannot succeed until the concepts governing the disease threat (i.e. resistance and pressure) can be interrelated in a quantitative way. Hence the second concern is to devise standard or, ideally, absolute measures of resistance, pressure and progress.

Results and conclusions
The framework suggested here takes as an analogy Ohm's Law, by which a current (disease progress) can be predicted, if the voltage (pathogen pressure) and the resistance (host resistance) are known. Applying this to foliar diseases, the disease threat is treated as being the expected progress of disease, and is being considered in terms of its damage to green leaf tissues. This is then taken to depend simply and quantitatively on pathogen pressure and host resistance. Turning to the issue of quantification, the steps leading from green area, through light interception by the green canopy to yield [1] are habitually quantified in absolute units of hectares, megajoules and tonnes, respectively, but the steps leading from pathogen to loss of green area are not. At present, in the field, diseases are measured as a ratio between the area of infected tissue and the total area of infected leaf, commonly expressed as a percentage, the 'severity'. This is unsatisfactory because, as diseases progress, the diseased area as well as the total leaf area are both markedly affected by death and shrinkage [1]. Thus the ratio provides no information about the size of the host, and changes over time misrepresent the activity of a pathogen. If disease progress is to be related to host function, it must be quantified as the rate of loss of photosynthetic area. Initial work has shown the feasibility of this measurement in field experiments by assessing total leaf area (cm2) at the same time as disease severity and percentage green area [2], and by assessing these frequently.

It remains for further research to (i) adopt these new units for disease progress, then (ii) devise scales and techniques to measure pressure, (iii) explore how progress is affected by pressure, (iv) use the ratio of pressure to progress to provide an absolute scale for resistance, and (v) determine the resistance of commercial genotypes. It should then be possible for a decision-taker in the field to obtain an estimate of pathogen pressure, to assess host resistance in absolute terms (this will depend on leaf condition as well as on genotype), thus to calculate the expected rate of disease progress, and ultimately to select an appropriate dose of fungicide just sufficient to bring this rate to zero.

1. Madeira AC, Clark JA, 1994. In Resource Capture by Crops, Eds. Monteith JL, Scott RK, Unsworth MH. Nottingham University Press, pp. 167-188.
2. Bryson RJ, Paveley ND, Clark WS et al., 1997. European Journal of Agronomy 7, 53-62.