1. Chapter 4: Crop Diseases assessment
 Disease assessment is defined as the act (or process) of quantitatively measuring
disease intensity (Campbell and Madden, 1990; Nutter et al., 1991; Nutter and
Gaunt, 1996).
 Assessment or measurement of disease is the basis of epidemiology which is the
study of disease at the level of populations of pathogens and hosts
 Plant disease assessment, also known as phytopathometery
 It is also the basis of the study of the effects of disease on crop yield and of
disease forecasting.
Definition of plant disease assessment

2. Crop Diseases assessment methods…..
Disease offers three parameters for measurement.
1. Disease incidence
The proportion of infected host units, out of the total units sampled
Disease incidence (I)=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑 𝑝𝑙𝑎𝑛𝑡 𝑢𝑛𝑖𝑡
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡 𝑢𝑛𝑖𝑡 𝑎𝑠𝑠𝑒𝑠𝑠𝑒𝑑
x100
2. Severity – the percentage area of diseased tissue/the proportion of the area
of a plant or plant organ (e.g. leaf area, seed, root etc) that is affected
Disease severity(S)=
𝐴𝑟𝑒𝑎 𝑜𝑓 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑 𝑡𝑖𝑠𝑠𝑢𝑒
𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑠𝑠𝑢𝑒 𝑎𝑟𝑒𝑎
x100
3. Loss – diminution of the crop due to a disease.
 In actuality the harvested yield is measured and the loss will be computed.
 Loss refers to reduction in either quantity or quality (or both) of yield.
What do we actually measure? (The Parameters)

3. Crop Diseases assessment methods…..
 In some disease assessment procedures, two further parameters may be
encountered:
 Prevalence – an ambiguous term that often refers to disease incidence within a
geographical area.
 e.g. if, a survey of bacterial streak of sorghum in Alemaya Woreda showed
that the disease occurred in 15 out of 20 representative fields inspected, the
prevalence of the disease in Alemaya would be 75%.
 Intensity – occasionally considered synonymous to severity is usually used to
denote measures of the number of fungal colonies/pustules on leaves.
 In some publications, the term intensity is used to describe any measure of
disease, be it incidence or severity.

4. Crop Diseases assessment and yield loss…..
1. For making decision concerning disease management
2. For determining the efficacy of various control measures (pesticides, genotype
resistance, agronomic measures, etc.).
3. To know proper time of applying control measure
4. To know cost of control measure
if we are not in a position to estimate the losses from diseases, then how can we decide
rationally on how much to spend on control?
 The economic advantage of any control method has to be determined.
 It is not good implementing a control measure that costs the farmer more than it returns in
increased yield.
 The economic advantage of any control strategy can be estimated by applying the
following formula:
 Economic advantage of disease control ($) =Expected return if disease is controlled ($) –
[Expected return if the disease is left uncontrolled ($)+ Cost of control treatment ($)]
Why do we measure disease and loss?....

5. Crop Diseases assessment and yield loss…..
4. Identifying resource/research priorities (plant breeders, fungicide manufacturers,
economists, government agencies and academics rely on disease assessment
data)
5. Evaluation of experiments (e.g. while screening for resistant germplasm)
6. Evaluation of the performance of control like
• whether valued varieties are still doing well or losing effectiveness;
• whether a new fungicide, bactericide or nematicide is performing up to
expectations;
• whether development of resistant races/populations is underway, etc.)
7. Of paramount importance in disease assessment and yield loss appraisal is the
standardization of concepts and terms in order to improve communication
between plant pathologists and across scientific disciplines.
Why do we measure disease and loss?....

6.  The assessment of plant diseases and their effects on yield normally involves
five distinct processes:
I. Developing a descriptive growth stage key for the particular crop species in
question,
II. Developing methods to assess the incidence and severity of disease,
III. Developing statistically sound methods of sampling crop populations for
assessment of the amount of disease,
IV. Estimating the negative impact of particular levels of the disease on crop yield
and quality and
V. Evaluating the economic benefit from various methods available for reducing
the amount of disease.
Crop Diseases assessment and yield loss…..

7. 4.1. Assessment of crop growth and development
 To understand the impact of a disease fully it is necessary to understand the
growth, development and physiology of the healthy plant.
 One of the first steps in quantitative disease assessment is to obtain or develop a
key that describes the growth and development of disease-free plants during the
growing season.
 In annual plants, the keys describe development from the time of sowing or
planting until harvest.
 In perennial species such as tree crops, variations in growth patterns between
seasons are described, often beginning with bud burst in spring.
 In tropical perennial crops the starting point is more difficult to determine since
growth often occurs throughout the year.
 It is therefore often necessary to nominate a more arbitrary starting point (e.g. a
particular growth flush at the beginning of the wet season).

8. Assessment of crop growth and development….
 Detailed drawings or photographs are needed to show such characteristics as the
 structure of the canopy at various stages of crop growth,
 the formation of new leaves and the senescence of older leaves,
 the development of reproductive structures and
 different stages in the formation of grain or other harvested products.
 Detailed information on the development of healthy plants is needed before the effects of
disease on crop growth and development can be assessed.
 For example, it is important to distinguish between normal senescence of leaves and
damage caused by parasites.
 Some parasitic fungi develop mainly on senescing leaves and so their impact on yield is
probably small.
 They may just be speeding up the process of decay of senescent leaves.
 Descriptive and pictorial growth stage keys have been developed for a number of crops
including wheat, oats, barley and rye maize, rice, tobacco, cotton, legumes, broad
beans (Vicia Jaba) …etc,

9.  The Feekes Scale illustrated by Large (1954) has been used for many years to depict
growth stages graphically.
 With the advent of computerization the Feekes scale has now been largely replaced by
the decimal key of Zadoks et al. (1974) which has been illustrated by Tottman and Broad
(1987).
 This scale differs from the Feekes scale in describing individual plants rather than
classifying crop growth stages.
 The growth stage keys are reproduced in Fig. 1 and 2 and Table 1 and 2.
Assessment of crop growth and development….

10. Figure 1 The Feekes scale for describing growth stages of cereals.

12. Table 1. Decimalized key of Zadoks et al. (1974) for the growth stages of cereals

14. Table 2 The BBCH growth stage scale (taken in part) for cereals, rice and maize (from Lancashire et al.,
1991)

15. 4.2. METHODS OF DISEASE ASSESSMENT
 In any disease assessment or phytopathometric method, two criteria must be satisfied;
these were described by James (1983) as consistency between observers and simplicity
for speed of operation.
 These criteria, therefore, dictate that all assessment methods should be well defined and
standardized at the earliest possible stage of their development.
 A successful system for the assessment of disease gives results that are accurate and
precise.
 The common analogy of the target used by an archer where the objective is to shoot all
arrows into the center circle of the target might be useful to clarify these concepts (see
illustration below).
Fig. 3 Accuracy and precision of an archer when the
objective is to place all arrows in the central circle
(a) accurate and precise: (b) not accurate but precise;
(c) not accurate and not precise.

16.  Disease can be measured using
 Direct methods (i.e. assessing disease in or on the plant material, which could be
qualitative or quantitative) or
 Indirect methods (e.g. monitoring spore population using spore traps).
 Obviously direct methods are likely to be more strongly correlated with yield losses in the
crop and are therefore to be preferred.
 However, recent methods involving remote sensing and detection of crop stress due to
disease are likely to increase the accuracy of indirect disease measurements.
 Direct methods are concerned with both the quantitative and qualitative estimations of
disease.
4.2. METHODS OF DISEASE ASSESSMENT…

17. 4.2.1. Direct quantitative methods
 Direct quantitative methods are largely concerned with measurements of incidence or
severity, defined as follows.
Disease incidence (I)=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑 𝑝𝑙𝑎𝑛𝑡 𝑢𝑛𝑖𝑡
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡 𝑢𝑛𝑖𝑡 𝑎𝑠𝑠𝑒𝑠𝑠𝑒𝑑
x100
4.2.1.1.Disease incidence
 Disease incidence is the most readily determined parameter often by simply counting the
number of plants showing symptoms of the disease.
 It is normally used in order to determine the spread of disease over a given geographical
area.
 In diseases with systemic infections which may result in total plant loss (e.g. viruses, cereal
smuts, or vascular wilts), incidence may be equated with disease severity.

18. 4.2.1.1.Disease incidence…
 Assessment of disease incidence is traditionally based on visual disease symptoms, the
definition can easily accommodate other more modern methods such as
 The enzyme-linked immunosorbent assay (ELISA) and
 Polymerase chain reaction (PCR)
 Disease incidence is a binary variable, that is, a plant unit is either (visibly) diseased or
not (Madden and Hughes, 1999).
 Disease incidence would be suitable for assessing systemic infections which may result in
total plant loss (e.g. viruses or cereal smuts) as well as many root diseases, or
 Where a single lesion causes leaf death (e.g. axil lesions in barley caused by
Rhynchosporium secalis)
 But may also be useful in the early stages of an epidemic caused by a cereal foliar
pathogen when both incidence (number of tillers affected) and severity (leaf area affected)
are related and increase simultaneously (James, 1983).

19. 4.2.1.2. Disease severity (pictorial vs.
descriptive keys)
 Most disease assessment keys are designed to
measure disease severity using either pictorial
(picture) or descriptive keys.
 With either type of key, standardization is
maintained, and the use of arbitrary categories
such as slight, moderate or severe can be avoided.
 The pictorial key uses standard area diagrams
which illustrate the developmental stages of a
disease on small sample units (leaves, fruits) (Fig.
4, 5) or,
Disease severity(S)=
𝐴𝑟𝑒𝑎 𝑜𝑓 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑 𝑡𝑖𝑠𝑠𝑢𝑒
𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑠𝑠𝑢𝑒 𝑎𝑟𝑒𝑎
x100
Fig. 4. Examples of pictoral assessment keys for estimating disease severity
(after James, 1971)

20. 4.2.1.2. Disease severity .…
Fig. 5. Further example of pictorial disease assessment keys (For apple scab on leaves and
fruit)

21.  occasionally, on large composite units such as branches or whole plants (Fig 6).
 Such standard diagrams are derived from a series of disease symptom pictures which may be
in the form of line drawings, photographs or even preserved specimens.
 Standard area diagrams are then prepared, traditionally by a painstaking method using graph
paper outlines.
 Nowadays, the use of planimeters, electronic scanners and image analysis has improved and
quickened their production.
Fig. 6. Saari-Prescott (0-9) scale for appraising the intensity of foliar diseases (except rusts) in wheat and barley (Saari and
Prescott, 1975); the scale incorporates a double digit 00-99 scale for evaluating the intensity (severity and vertical disease
progress), the first digit gives the relative height of the disease in 0-9 scale and the second digit shows severity in 0-9 scale
(0%-90% coverage in equal divisions of 10%)
4.2.1.2. Disease severity .…

22.  Measures to standardize assessment keys and to eliminate operator error (subjectivity)
have been incorporated in the newest keys.
 Nevertheless, the diagrammatic assessment of severity suffers from fundamental errors.
First, standard area diagrams do not display the variegated patterns of disease so
commonly caused by a plant pathogen.
 Thus an observer is compelled to visualize the total area that the various lesion shapes on a
leaf would cover if they would be combined and then express this as a percentage of the
total leaf area.
 Recently, a key has been developed in which pictorial representation of disease levels is
presented on a generic cereal leaf divided into a grid of 1% sectors of leaf lamina (Figure
7).
 Apparently this key has the advantages of a reduced need for interpolation, no reliance on
high contrast diagrams and avoidance of the need for different keys for assessing more
than one disease.
4.2.1.2. Disease severity .…

23. Fig. 7.) Prototype of a new wheat leaf disease assessment aid to avoid some of
the disadvantages associated with the use of conventional disease assessment
keys. Each sector of the grid is equal to 1% (after Jones et al., 1998)
Fig. 8. Pictorial assessment key for leaf blotch of barley caused by
Rhynchosporium secalis (after Jones et al., 1998)

24.  A second problem relates to the variation in leaf size and how this affects the
observer’s assessment of severity.
 The key for the assessment of barley leaf blotch (Fig. 8) usefully attempts to
apply separate diagrams for barley leaves of differing size to relate comparable
percentage areas of disease.
 Most descriptive keys utilize a percentage scale where grades are differentiated
according to the leaf area diseased (e.g. Table 3).
 Like pictorial keys, descriptive keys also suffer from subjective errors, and these
could be improved by training of observers who assess disease.
 As in the diagrams of the pictorial keys, the numerical categories or scores of
disease severity in descriptive keys are also established experimentally and
tested for adequacy and practicability.
4.2.1.2. Disease severity .…

25. Blight (%) Disease severity description
0 Not seen on field
0.1 Only a few plants affected here and there; up to 1 or 2 spots in 12 yards radius
1 Up to 10 spots per plant, or general light spotting
5 About 50 spots per plant or up to 1 leaflet in 10 attacked
25 Nearly every leaflet with lesions, plants still retaining normal form:
field may smell of blight, but looks green although every plant is
affected
50 Every plant affected and about half of leaf area destroyed by blight, field
looks green flecked with brown
75 About ¾ of leaf area destroyed by bight: field looks neither predominantly
brown nor green. In some varieties the youngest leaves escape infection so that
green is more conspicuous than in varieties like “King Edward”, which
commonly shows severe shoot infection
95 Only a few leaves left green, but stems green
100 All leaves dead, stems dead or dying
Table 3 Example of descriptive key (for assessment of potato late blight,
Phytophthora infestans)

26. 4.2.2. Direct Qualitative Methods (Assessing the
Infection Type)
 Where responses to individual virulences (physiological races) are under scrutiny
as in breeding programs for evaluation of resistance of genotypes, or in race
surveys, a qualitative method of assessment may be used.
 In such cases it may be required to evaluate the infection type as shown in the
examples in Tables 4 and 5.
 Since infection types may vary under the influence of environmental factors, the
assessment should be carried out mostly on young plants (2 – 4 leaf stage); and
 It has to be done on plants that have been grown and inoculated in controlled
conditions in the greenhouse.
 Also, the cardinal values like temperature, etc. as well as the time interval
between inoculation and assessment that are recommended for the particular
pathogen should be observed.

27. Symbol Host: Parasite interaction
Oi Immune; no visible signs of infection
Oc Highly resistant; minute chlorotic flecks
On Highly resistant; minute necrotic flecks
1 Resistant; small pustules with necrotic surrounding tissue
2 Moderately resistant; medium- sized pustules with necrotic surrounding tissue
3 Moderately susceptible; medium-sized pustules with chlorotic surrounding tissue
4 Susceptible; large pustules with little or no chlorosis
X Mesothetic reaction; mixed reaction types on one leaf
Class Reaction
0 No observable infection
1 Pin- point brown lesions, no chlorosis
2 Small dark brown lesions, no chlorosis
3 Restricted long brown streaks, slight associated chlorosis
4 Brown elongated lesions with net-like cross variations, marked chlorosis
Table 5. Reaction-type classes for Pyrenophora teres on barley; a kind of qualitative key for disease
assessment
Table 4 An assessment key of cereal rust virulence response (example of qualitative keys for
disease assessment)

28. 4.2.2. Indirect Methods of Disease Assessment
 Traditional indirect methods include monitoring pathogen spore populations over
infected crops (e.g. using spore traps or sticky surfaces) or trapping insect vectors of a
virus to estimate the level of disease severity.
 Other methods include measuring fungal biomass in plant tissues
 e.g. quantifying fungal chitin or ergosterol,
 measuring effect of the pathogen on the host
 e.g. stunting, reductions in root growth, in ear and grain number and size, to
mention a few), and
 the incubation methods that are used to quantify seed infection.
 In many parts of the world, remote sensing methods mainly through the use of infrared
photography are also being used with increased accuracy and efficiency for assessing
plant disease severity.

29. 4.3. Crop Loss Appraisal/Assessment
 The most important single objective of disease assessment is crop loss appraisal, both
for yield and quality.
 The conversion of disease data into crop losses is not an easy task because of the
presence of several confounding factors.
 There are two ways of assessing yield loss: statistical and experimental methods.
4.3.1. Statistical Methods
 Statistical methods involve analysis of yields in relation to estimated disease
level over many seasons (but other factors such as pests must be taken into
account).
4.3.2. Experimental Methods
 Experimental methods are mainly based on yield comparisons between infected and
healthy plants or between plants with different disease severities using field plots, micro
plots, single plants or tillers.
 Basically two kinds of loss assessment experiments are used.

30. 4.3.2.1. Paired-treatment experiments
 Such experiments, also called paired-plot experiments, involve the comparison of yields
of two plots in each of several replications,
 where the plants of one plot are protected from the pathogen by an appropriate fungicide
or by a resistant isogenic line.
4.3.2.2.Multiple-treatment experiments
Multiple treatment experiments allow the comparison of the effect of different levels of
disease on yield in one location.
They involve plots with varying amounts of disease and one healthy (protected) treatment.
To obtain different levels of disease it is necessary to delay the progress of disease in some
plots.
Mostly, different fungicide spray schedules are used for this purpose. Both the number and
frequency of fungicide spray can be varied.

31. Mathematical models, the so-called critical point model and multiple point model are used
to express the relationship between disease and yield loss.
Linear and multiple regression analyses are used to derive empirical formulae that relate
disease and loss.
Neither disease levels nor crop losses are static, and they will change from year to year in a
given location.
Experiments should be conducted for at least three years at each of a number of locations.
Such information needs to be up-dated, perhaps every five years.
This is particularly important in the current dynamic agricultural system, which involves the
introduction of new plant varieties and agricultural chemicals.
4.3.2.2.Multiple-treatment experiments…