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Comparison of two disease assessment methods for assessing Cercospora leaf spot in sugar beet
Crop Protection 22 (2003) 201–209
Comparison of two disease assessment methods for assessing Cercospora leaf spot in sugar beet J. Vereijssena,*, J.H.M. Schneidera, A.J. Termorshuizenb, M.J. Jegerc b
a Institute of Sugar Beet Research, P.O. Box 32, 4600 AA, Bergen op Zoom, The Netherlands Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG, Wageningen, The Netherlands c Imperial College at Wye, Wye, Ashford, Kent TN25 5AH, UK
Received 18 December 2001; received in revised form 12 April 2002; accepted 8 August 2002
Abstract Two disease severity scales for Cercospora leaf spot (CLS) assessment were compared. CLS was assessed in two experimental fields in the Netherlands in 1999 using two scales: a single leaf severity assessment, as used in the IPS-system (Integriertes Pflanzenschutzsystem) in Germany, referred to as DSIPS, and a whole plant assessment, Agronomica diagram from Italy, referred to as DSAGR. To obtain a range of disease severities, fungicides were applied at defined action thresholds based on disease incidence and severity. There was an exponential relationship between DSIPS and DSAGR (R2 ¼ 86%) for pooled data with little change in the whole plant assessment above DSIPS=5%. An exponential curve best fitted DSIPS and root and sugar weight in fields severely infected with CLS, whereas a linear curve was found for mildly infected fields. A linear curve fitted DSAGR best with root and sugar weight in both severely and mildly infected fields. No relationship was found between both DSIPS and DSAGR and sugar content. The use of DSAGR was less time consuming in monitoring and was done with greater accuracy, efficiency and level of reproducibility than DSIPS. These results demonstrate that CLS assessment can be less time-consuming and more practical in application. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Cercospora beticola; Disease assessment method; Sugar beet; Disease scale
1. Introduction Cercospora leaf spot (CLS) in sugar beet (Beta vulgaris L.) caused by Cercospora beticola Sacc. occurs world-wide and may cause a reduction of 42% gross sugar yield (Shane and Teng, 1992) which leads to problems (less extractable sugar) at the sugar factory and less income for farmers. CLS decreases both beet root weight and sugar content, resulting in reduced gross sugar yield (root weight sugar content). In severe epidemics the foliage will be totally destroyed and the beet starts to grow new leaves; flushes of new growth. The effect of CLS severity on a sugar beet plant has been measured via the yield parameters root weight and sugar content in variety or fungicide application field trials (e.g. Shane and Teng, 1992). Root weight and sugar content are strong negatively influenced by the extent of *Corresponding author. Tel.: +31-164-274400; fax: +31-164250962. E-mail address: [email protected] (J. Vereijssen).
new growth. In the Netherlands, where the area infested with CLS has increased during the last 25 years to 34% of the cultivated sugar beet area (Holtschulte, 2000), losses of up to 20% in gross sugar yield due to CLS have been recorded (Vereijssen, unpublished data). Resistance to CLS is only partial (Skaracis and Biancardi, 2000), and not widely available in commercial varieties, so does not replace the need for a fungicide treatment. Two fungicides, with benomyl or carbendazim (both benzimidazoles) as the active ingredient, are registered to control CLS in the Netherlands, but resistance to these fungicides has been reported in the south-eastern part of the country (Sch.aufele and Wevers, 1996). At present, fungicide treatment according to a fixed schedule offers good control of CLS, but this involves application of significant quantities of fungicide, even under conditions unfavourable for any increase in disease (e.g. dry weather). To meet government policy objectives the use of fungicides has to be reduced in the Netherlands (Anon., 1991), and rate of approval of new fungicides has slowed down in recent years.
0261-2194/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 1 - 2 1 9 4 ( 0 2 ) 0 0 1 4 6 - 1
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Durable and profitable control of CLS could be obtained if fungicides are applied according to action thresholds; this means applying a fungicide when the incidence or severity of disease exceeds the amount of disease that can be tolerated in the crop. Using action thresholds, fungicide use can be decreased in sugar beet. To develop action thresholds and judge their effectiveness in disease control the amount of disease must be quantified. Without such data, estimation of disease progress rates, comparison of treatments such as varieties or control measures, and prediction of future disease or yield loss is not possible (O’Brien and Van Bruggen, 1992). Quantifying the amount of disease can also lead to fungicide reduction and determining the amount of disease that can be tolerated in the field (Zadoks and Schein, 1979). According to Kranz (1990), the best assessment system is one based on standard diagrams or keys that illustrate what the evaluator should see for each classification in a continuous disease scale. A powdery mildew standard diagram has been described in sugar beet (Hills et al., 1980), and several CLS single leaf standard diagrams have been proposed (for example Anon., 1970; Rossi and Battilani, 1989; Battilani et al., 1990). The Kleinwanzlebener Saatzucht (KWS) scale (Anon., 1970) for CLS is described for both
single leaves and whole plants. Shane and Teng (1992), however, concluded that the KWS scale was unsuitable for accurate yield loss studies. The KWS scale categories are not precisely defined, especially at low disease intensities and the scale is more appropriate for evaluation of breeding lines in inoculated trials where there is a need for rapid assessment of plants with relatively high severities (Shane and Teng, 1992). Systems developed for control of CLS have used several assessment methods. Verreet et al. (1996) assessed severity on every single leaf of a beet plant and calculated whole plant severity from these data. Windels et al. (1998) assessed severity according to a Spot Percentage scale (Jones and Windels, 1991) on the older, lower leaves of 100 plants in a field. Some authors do not mention the scales or standard diagrams used (Rossi and Battilani, 1991; Rossi et al., 1994; Skaracis et al., 1996). The Agronomica diagram (Fig. 1) is commonly used in the field in Italy, and its values are subsequently used to recalculate severity for use in simulation models as a basis for a computer-based decision support system (Merrigi, pers. comm.) to forecast both disease progress rates (Rossi et al., 1994) and primary infections (Rossi and Battilani, 1991). Rossberg et al. (2000) used 50% prevalence of fields as a threshold in a district in their
Fig. 1. Agronomica diagram for disease assessment of Cercospora leaf spot on sugar beet.
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model. This corresponds to roughly 5% diseased leaves in a field and indicates the timing of a preventive fungicide application, which is currently used in Germany (Wolf et al., 1998; Rossberg et al., 2000). In our study we compared two existing disease severity assessment scales, and determined their practical value for CLS disease assessment in the Netherlands: the single leaf severity scale from Germany, used in the IPS-system (Integriertes Pflanzenschutzsystem) (Verreet et al., 1996), further referred to as DSIPS and the Agronomica whole plant diagram or field key from Italy (Battilani et al., 1990) (Fig. 1), DSAGR. DSIPS was chosen, as it has proven successful in Germany (Wolf et al., 1998). DSAGR is used extensively in Italy to forecast disease progress (Rossi et al., 1994) and rationalise fungicide use (Rossi and Battilani, 1991). Data must be accurate and precise for evaluating the extent of control by action thresholds, and its collection must be efficient in terms of time and costs. DSIPS is laborious but the quantity and quality of information is sufficient for development of action thresholds (Verreet et al., 1996). However, this method of assessment can be quite destructive within a crop due through the snapping off of leaf petioles when assessing severity. Also feeding of rabbits on sugar beet leaves can be disastrous for the assessment, as whole leaves can disappear. The use of ‘‘whole-plant’’ diagrams (Kranz, 1990) or field keys (Large, 1966) such as DSAGR, by contrast, is less time consuming, and improves efficiency, reproducibility and accuracy for both experienced and inexperienced evaluators (Kranz, 1990). Such diagrams must include sufficient intervals to represent all stages of disease development (O’Brien and van Bruggen, 1992). A ‘‘whole-plant’’ diagram removes the need for an evaluator to make assessments at the whole-plant level from diagrams constructed for individual leaves (Kranz, 1990). Our research aims to develop a decision support system for CLS in sugar beet in the Netherlands. A primary objective is to make CLS disease assessment less time consuming and more practical in application than DSIPS, but equally reliable and reproducible. This paper describes (i) a comparison of DSAGR with DSIPS in assessment of CLS, (ii) the relation between DSIPS and root weight, sugar content and sugar weight, and (iii) the relation between DSAGR and the same yield variables. These findings will subsequently be incorporate in a DSS for the Netherlands.
2. Materials and methods 2.1. Experimental fields Field trials were conducted at two locations in the Netherlands in 1999: at Maasbree (in the province of
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Limburg) and at Toldijk (in the province of Gelderland). The province of Limburg has a long CLS history with severe epidemics. The province of Gelderland is a relatively new CLS area with less severe epidemics. These fields, expected to differ in CLS severity, were intentionally chosen to determine the performance of each scale at both low and high severities. Farmer’s fields were drilled with Beta vulgaris: cv. Tiara at Maasbree (sown April 10) and cv. Rebecca at Toldijk (sown March 29). The experimental design was 6 treatments (4 fungicide sprays, untreated and calendar sprayed), to obtain a range of CLS severities, in randomised blocks with four replications. Experimental units were 3 m (6 rows of sugar beet) 12.5 m and blocks were separated by 2.5 m bare soil. Beets were harvested manually on November 3 at Maasbree and September 28 at Toldijk, according to fixed beet delivery contracts between the farmers and the sugar industry. Only the inner four rows of each plot were harvested. Root yields and sugar contents were analysed at the IRS using a standard protocol (de Bruijn et al., 1999).
2.2. Disease assessment At Maasbree, plants were monitored weekly from July 27 to September 13, and at Toldijk from July 29 to September 23. For each experimental unit (plot) of 3 m 12.5 m, five plants (3 in row 2, 2 in row 5) (20 plants per treatment) were used for severity assessment. Five plants were monitored due to time restrictions, and one person carried out the assessments. CLS severity was assessed using two methods: DSIPS and DSAGR. DSIPS, as used in the IPS-system (Verreet et al., 1996), is a single-leaf severity assessment based on pre-defined levels of leaf area diseased (0%; 0.1%; 0.3%; 0.5%; 1%; 5%; 10%; 25%; 50%; 75%; 90%; 100%) for each leaf on a plant. For DSIPS the first three green petioles were tagged with different coloured rings to assist monitoring. As the first green leaf died, rings were moved anticlockwise following the rosette until the next green petiole was marked. Severity of all leaves on a plant was assessed and data were recorded directly in a laptop computer. Severity of all leaves was subsequently expressed as the mean per plant, resulting in a wholeplant severity number. The average whole-plant severity of 20 plants per treatment per week is used for the action thresholds. DSAGR, Agronomica diagram (Battilani et al., 1990), is a whole plant assessment based on 11 classes (0–5) from healthy through to totally destroyed foliage (Fig. 1). When DSAGR=5, the value 0.5 is added to the scale for every additional week to take into account sugar losses due to growth flushes. The average DSAGR value of 20 plants per treatment per week is calculated and used as the average disease index for a treatment.
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2.3. Treatments
4. Results
Four different fungicide treatments were applied to obtain a range of CLS severities; an unsprayed control and calendar sprayed treatment were also included. The unsprayed control was assumed to give the maximum CLS severity for a field and thus the lowest yield, whereas the calendar spraying was expected to give the maximum yield. A triazole fungicide (a.i. difenoconazole 250 g/l) was applied (0.4 l/ha at 3.3 bar) at both locations using a hand-held horizontal spray boom with six Teejet 11003 vs nozzles 50 cm apart with 50 mesh anti-drop screen at a walking speed of 1 m/s.
4.1. Relation between DSIPS and DSAGR
3. Statistical analysis 3.1. Relation between DSIPS and DSAGR To obtain a range of disease from low to high severities, severity data for the six treatments were pooled for each location. Disease severities of all assessment weeks, according to DSIPS and DSAGR at the plant level, were used to compare the two assessment scales per location. The relationship between DSIPS and DSAGR was described using nonlinear regression (Genstat, Version 3.3.4). Equality of the asymptotes of the regression functions at each location were tested with a t-test. Also, severity data according to DSAGR and DSIPS of both locations were pooled and a general relationship between DSIPS and DSAGR was described using nonlinear regression (Genstat, Version 3.3.4). 3.2. Relationship between disease severity assessment and yield parameters Mean disease severity at the plot level assessed using DSIPS and DSAGR was used as the independent variable in regression analyses with root weight, sugar content and sugar weight (root weight sugar content), as the dependent variable. To account for variety and location differences, relative values of the yield variables were used in the statistical analyses. Mean root weight, sugar content and sugar weight of the calendar sprayed treatment were set at 100 for each field with values for the other treatments calculated accordingly. Root weights, sugar contents and sugar weights were regressed on disease severities using Genstat procedures. The best fitting curve was determined based firstly on homogeneity of the error variance in the residual plot and secondly on variance accounted for (R2 ) by the model. The significance of the regression coefficient, deviance from zero in either the positive or negative sense, was tested using a two-sided t-test.
As expected a minor CLS epidemic (low severities) developed at Toldijk and a major epidemic (high severities) at Maasbree (Fig. 2), and within each field, treatments also affected CLS severity. For the major epidemic in Maasbree, high disease levels on some plants (DSAGR=5) were accompanied by flushes of growth. In this situation, 3–4 times more leaves per plant per week can be produced in comparison with plants with a lower level of disease, with presumably the plant investing energy in leaf rather than sugar production. An exponential model best described the relationship between DSIPS and DSAGR at Maasbree and Toldijk (Fig. 3), with little change in DSAGR above DSIPS values above 5%. Although transformation of DSIPS (arcsine, square root, logit) did increase the variance accounted for (R2 ), the residual plots showed heteroscedasticity (data not shown). A homogeneous pattern of residuals was obtained with untransformed data. The variance accounted for (R2 ) was high for both Maasbree (86%) and Toldijk (88%), and for pooled data (86%) (Fig. 3). The exponential curve for pooled data had an asymptote at DSAGR=4.3, indicating the highest average DSAGR value across both locations. The asymptote of the exponential curve for the major epidemic at Maasbree (DSAGR=4.5) was significantly higher than for the minor epidemic at Toldijk (DSAGR=3.7) (P > 0:05) (Fig. 3). Because CLS severity was lower in Toldijk (Fig. 3), the fitted curve was estimated from severities less than 15%, whereas for Maasbree there were also many data points in the range 15–32% (Fig. 3). In the linear part of the curve (DSIPS=0– 2.5%) the fitted curve for pooled data gave a good fit to both data sets (Fig. 3). 4.2. Relationship between DSIPS and yield parameters At Maasbree there were exponential relationships between relative root weight (Fig. 4a, 100%=87.0 t/ha) and relative sugar weight (Fig. 4c, 100%=13.8 t/ha) with severities assessed according to DSIPS. Corresponding R2 values were 43% and 50%, respectively. At Toldijk, with only a minor CLS epidemic, the linear relationship between relative root weight and DSIPS (R2 ¼ 0:3%) was not significant (b ¼ 0:5; P ¼ 0:5) (Fig. 4a, 100%=81.9 t/ha). For relative sugar weight a significant linear relationship (R2 ¼ 16%) was observed (b ¼ 1:0; P ¼ 0:05) (Fig. 4c, 100%=13.4 t/ha). Transformation of DSIPS data (arcsine, square root, logit) did not improve homogeneity of the residuals, sometimes it increased variance accounted for (R2 ) to a maximum value of 0.9% for relative root weight and 20.1% for
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18 16
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DSIPS (severity (%))
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33 35 37 39 assessment week Fig. 2. Disease progress curves of Cercospora leaf spot of unsprayed control treatment at Maasbree and at Toldijk 1999. Points represent means of four replicates of each 5 plants; bars at week 38 are standard errors.
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Fig. 3. Comparison of DSIPS and DSAGR at Maasbree and at Toldijk in 1999. Pooled data: y ¼ 4:3ð70:05Þ 3:7ð70:05Þn 0:4ð70:01Þx (regression line given), R2 ¼ 86; Maasbree (&): y ¼ 4:5ð70:06Þ 3:9ð70:06Þn 0:5ð70:01Þx ; R2 ¼ 86; Toldijk (’): y ¼ 3:7ð70:06Þ 3:3ð70:06Þn 0:2ð70:015Þx ; R2 ¼ 88: Standard errors are in parentheses.
relative sugar weight. At both locations relative sugar weight decreased more rapidly with increasing severity than relative root weight. There were no statistically significant relationships between relative sugar content and DSIPS (data not shown).
4.3. Relationship between DSAGR and yield parameters At Maasbree, significant linear relationships between relative root weight (b ¼ 3:6; P ¼ 0:005) (Fig. 4b) and relative sugar weight (b ¼ 5:7; P ¼ 0:001) (Fig. 4d) and
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Fig. 4. Relation between DSIPS and DSAGR and yield variables at Maasbree and at Toldijk in 1999. (A) DSIPS and relative root weight, Maasbree (&): y ¼ 91:1ð72:23Þ þ 14:1ð74:26Þn 0:6ð70:21Þx ; R2 ¼ 43 and Toldijk (’): y ¼ 100:6ð71:12Þ 0:5ð70:53Þx; R2 ¼ 0:3: (B) DSAGR and relative root weight, Maasbree (&): y ¼ 3:6ð70:95Þx þ 109:0ð73:35Þ; R2 ¼ 42 and Toldijk (’): y ¼ 1:6ð70:82Þx þ 104:2ð72:39Þ; R2 ¼ 11: (C) DSIPS and relative sugar weight, Maasbree (&): y ¼ 84:4ð72:78Þ þ 23:3ð77:24Þn 0:5ð70:21Þx ; R2 ¼ 50 and Toldijk (’): y ¼ 99:4ð70:90Þ 1:0ð70:42Þx; R2 ¼ 16: (D) DSAGR and relative sugar weight, Maasbree (&): y ¼ 5:7ð71:20Þx þ 111:5ð74:23Þ; R2 ¼ 55 and Toldijk (’): y ¼ 1:9ð70:65Þx þ103:4ð71:89Þ; R2 ¼ 27: Standard errors are in parentheses. Maasbree: root weight 100%=87.0 t/ha, sugar weight 100%=13.8 t/ha; Toldijk root weight 100%=81.9 t/ha, sugar weight 100%=13.4 t/ha.
DSAGR were observed. At Toldijk, the linear relationship between relative root weight and DSAGR was not significant (b ¼ 1:6; P ¼ 0:1), but a significant linear relationship was found for relative sugar weight (b ¼ 1:9; P ¼ 0:01). Although all three yield variables decreased as severity increased, the regression with relative sugar weight as the dependent variable had the highest percentage variances accounted for (R2 ¼ 55% at Maasbree and R2 ¼ 27% at Toldijk). For relative root weight R2 values of 42% at Maasbree and 11% at Toldijk were obtained. Transformation of DSAGR data did neither improve homogeneity of the residuals nor increase R2 :
5. Discussion 5.1. Relationship between DSIPS and DSAGR In the Netherlands, the exponential relationships between severity assessed according to DSIPS and
DSAGR were similar to those when developing the Agronomica diagram in Italy (Rossi, pers. comm.). CLS is more severe in Italy, therefore the asymptote of the exponential curve DSAGR=4.8 was higher than the DSAGR=4.3 from our pooled data. The difference in asymptote means a jump in the Agronomica diagram from 4.5 to 5 (Fig. 1), indicating a greater preponderance of growth. The main use of the DSIPS assessments in this study is to calculate whether an action threshold will be reached in the field. The relationship between DSIPS and DSAGR can now be used to resolve the DSAGR value corresponding to a given severity value. Consequently, the action thresholds, expressed in severity values, as defined by Verreet et al. (1996) and used in this study, can be translated into DSAGR values. The calculations are not affected by the asymptote of the exponential curve, as the action thresholds based on DSIPS all are situated in the linear portion of the curve (DSIPSo5%). Future research will concern use and development of action thresholds based on DSAGR values.
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5.2. Relationship between disease severity assessment and yield parameters Although we obtained low R2 values, the regression coefficient was significantly different from 0, indicating a significant linear association between yield variable and assessment key. The purpose of relating the yield variables to the assessment keys was to indicate the differences between the two keys. A problem with disease assessments in newly affected CLS areas (Toldijk) in contrast to areas with a previous history of CLS (Maasbree) is the patchy occurrence of heavily diseased individual plants. Relatively healthy plants can be found surrounding plants at DSAGR=4 (Vereijssen, unpublished data). It is evident that this will have an influence on the calculation of whole plot (experimental unit) severity, and subsequently on treatment severity. The low variance accounted for in Toldijk may be due to no account being taken of the aggregation of diseased plants. Kranz (1987) has noted that the sampling technique is influenced by the spatial distribution of a disease in a crop. He recommends in most cases a systematic sampling (e.g. ‘‘W ’’—pattern with x sampling units at y metres distant along the path). However, for decision making in disease control sequential sampling is often appropriate (Kranz, 1987). The spatial variation of CLS diseased plants will be dealt with in a future paper. The difference between the two sites in yield at the same disease level could also be a result of varietal difference, not only in maximum yield, but also in susceptibility, which may be influenced by local environmental factors. Although both varieties are susceptible to CLS, there may be quantitative differences in sensitivity. Also the difference in soil type between the locations may cause differences in yield, either directly or through interactions with variety. We have shown that root weight, sugar content and sugar weight decrease exponentially in heavily diseased fields and linearly in mildly diseased fields when using DSIPS. Wolf et al. (1998), however, described a linear relationship between DSIPS and sugar yield loss in Germany with severity levels as high as 60% (see also Wolf and Verreet, 2002). According to Kranz (1990) this is quite a high level as leaf spot diseases in general rarely exceed 37% severity. From our observations, an assessment of 30% severity at the plant level gives a beet plant without green leaves except for those showing flushes of new growth. An advantage of DSAGR is that a linear model describes the relationship between DSAGR and yield loss in both minor and major epidemics. The linear association between both relative root and sugar weight and DSAGR was most pronounced in the major epidemics at Maasbree, as CLS will have a stronger effect on these variables. The strongest linear association, at both locations, is between relative sugar weight
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and DSAGR, indicating a combined action of both root weight and sugar content on yield loss. Another advantage of DSAGR is that it stretches severity in the 0–5 range, ignoring the 0.5 increment, for both locations whereas with DSIPS, data points are clustered within a small range. DSAGR, although simply constructed is easy to use and more reliable in predicting yield loss than the detailed and labour intensive DSIPS. 5.3. Interpretation and implementation for disease assessment The relationship between DSIPS and root weight, sugar content and sugar weight in a heavily diseased field was linear up to a DSIPS value of about 5%. The 5% severity level corresponds to a DSAGR between 4 and 4.5 (Fig. 2). This is almost at the stage of DSAGR where 0.5 is added to the score weekly, and this might explain why yield variables are linearly related to DSAGR. When beet produces new leaves as a response to totally destroyed foliage, whole-plant severity levels off and even decreases. As many as 12 new leaves per week can be grown during flushes of new growth compared to 3–4 leaves previously in the season. Jensen and Boyle (1966) already observed decreasing severity for C. arachidicola on peanut: ‘‘if the amount of disease is expressed as a percentage, a decrease in the amount of infection could occur at a later sampling date during periods of extremely rapid growth if only a few new leaves showed disease symptoms’’. This did not occur in our study, however, as although there were some flushes of growth, severity increased also on older alreadydiseased leaves. Wolf and Verreet (2002) did not indicate what happened with severity using DSIPS when high severities were reached, although they defined a tolerance limit for disease severity at harvest in their yieldloss relationships. Severity at harvest time is underestimated in their case, which will evidently lead to incorrect relationships between yield and severity. Also the maximum number of leaves grown per week was set at a maximum of 8 in DSIPS, irrespective of the stage of growth. This is a flaw of the system as more leaves can be grown in a week during flushes of growth (Rossi et al., 2000). DSIPS can thus not be easily used in Greece and Italy where CLS epidemics are generally more severe than in the Netherlands. Decreasing severity is not a problem from a practical point of view, e.g. when applying fungicides, but in model development (e.g. disease progress modelling) underestimation of disease severity can be a serious problem. DSAGR, however, continues to increase in severity as with totally destroyed foliage (DSAGR=5), 0.5 is added to the scale weekly. Regarding the effect of CLS on yield variables, Rossi et al. (2000) remarked that ‘‘flushes of growth is certainly thought to be at least in part responsible for the lowering of the sugar content and, more signifi-
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cantly, for the drop in processing quality that frequently occur in the late growing season’’, which argues for the use of DSAGR in yield loss assessments. When choosing between the two assessments scales precision, accuracy and reproducibility must be dealt with, as well as the time and cost involved. An advantage of DSIPS over DSAGR is that a distinction can be made between CLS necrosis and normally necrotic tissue. However, errors are made in estimating severity, as evaluators generally tend to overestimate (low disease levels) or underestimate (high disease levels) severity using a percentage scale (Kranz, 1987). Per plant an estimation error can be made 50–80 times, depending on the number of leaves per plant, which affects the accuracy of disease assessment, although some errors may cancel themselves out at the whole plant level. As three-dimensional entities such as the whole-plant are particularly difficult to estimate, Kranz (1987) proposed that the smallest possible sampling unit, such as a leaf or fruit, should be used to increase precision of the disease assessment. The accuracy and reproducibility of assessments is higher for DSAGR as the diagram has an accompanying text, which explains what the evaluator should see. The variation between evaluators should be smaller than using DSIPS. However, for both DSIPS and DSAGR training of new evaluators is needed. DSAGR can be vulnerable to errors compared to DSIPS when monitoring in bright sunlight, on wet plants or on darker coloured varieties. There is always some averaging when a diagram is used, or as Large (1966) concluded, ‘‘no standard diagram can show all differing distributions that can make up any given percentage of cover’’. This is also true for DSAGR, as the observer has to average CLS over the whole plant before assigning a DSAGR score to a plant, even with additional text to supplement the diagram. However, disease assessment using DSAGR is faster, thus less costly and less destructive to the plants than using DSIPS. This study has formed a basis for a simplified disease assessment in the development of a decision support system for CLS in the Netherlands. Use of DSAGR provides less time-consuming monitoring and also greater accuracy, efficiency and reproducibility. We anticipate that extension workers in the sugar industries and researchers in future Cercospora research can use the Agronomica diagram for field monitoring.
References Anonymous, 1970. Cercospora Tafel. Kleinwanzlebener Saatzucht Ag. Einbeck Rabbethge and Giesecke. Anonymous, 1991. Meerjarenplan Gewasbescherming. Regeringsbeslissing. The Hague, SDU, The Netherlands.
Battilani, P., Beltrami, G., Meriggi, P., Ponti, I., Rossi, A., Rossi, V., Rosso, F., Tugnoli, V., Zocca, A., 1990. Nuovi indrizzi di difesa anticercosporica. L’Informatore Agrario 46, 53–70. Bruijn de J.M., Vermeulen, D.P., a’Campo, P.J.H.M., Calon, A.P.A., van der Waal, W., Konings, J., Huijbregts, A.W.M., Elands, L., 1999. Uniforme methode voor gewichtsbepaling, monstername en monsteronderzoek van suikerbieten in Nederland. De Nederlandse Suikerindustrie, CSM, Diemen. Hills, F.J.L., Chiarappa, L., Geng, S., 1980. Powdery mildew of sugarbeet: disease and crop loss assessment. Phytopathology 70, 680–682. Holtschulte, B., 2000. Cercospora beticola—worldwide distribution and incidence. In: Asher, M.J.C., Holtschulte, B., Richard Molard, M., Rosso, F., Steinruecken, G., Beckers, R. (Eds.), Cercospora beticola Sacc. Biology, Agronomic Influence and Control Measures in Sugar Beet, Vol. 2. International Institute for Beet Research, Brussels, pp. 5–16. Jensen, R.E., Boyle, L.W., 1966. A technique for forecasting leaf spot on peanuts. Plant Dis. Rep. 50, 810–814. Jones, R.K., Windels, C.E., 1991. A management model for Cercospora leaf spot of sugarbeets. Univ. Minn. Ext. Serv. AGFO-5643-E. Kranz, J., 1987. Measuring plant disease. In: Kranz, J., Rotem, J. (Eds.), Experimental Techniques in Plants Disease Epidemiology. Springer, Heidelberg, Germany, pp. 35–50. Kranz, J., 1990. Monitoring epidemics: disease. In: Campbell, C.L., Madden, L.V. (Eds.), Introduction to Plant Disease Epidemiology. Wiley, New York, USA, pp. 107–128. Large, E.C., 1966. Measuring plant disease. Ann. Rev. Phytopathol. 4, 9–28. O’Brien, R.D., van Bruggen, A.H.C., 1992. Accuracy, precision, and correlation to yield loss of disease severity scales for corky root of lettuce. Phytopathology 82, 91–96. . Rossberg, D., Racca, P., Jorg, E., Kleinhenz, B., 2000. Erste Erfahrungen mit dem Modell CERCBET1. Nachrichtenbl. Dtsch. Pflanzenschutzdienst 52, 153–159. Rossi, V., Battilani, P., 1989. Assessment of intensity of Cercospora disease on sugar beet. I. J. Phytopathol. 124, 63–66. Rossi, V., Battilani, P., 1991. CERCOPRI: a forecasting model for primary infections of Cercospora leaf spot of sugar beet. EPPO Bull. 21, 527–531. Rossi, V., Racca, P., Battilani, P., 1994. A simulation model for Cercospora leaf spot epidemics on sugar beet. Phytopathol. Mediterr. 33, 105–112. Rossi, V., Meriggi, P., Biancardi, E., Rosso, F., 2000. Effect of Cercospora leaf spot on sugar beet growth, yield and quality. In: Asher, M.J.C., Holtschulte, B., Richard Molard, M., Rosso, F., Steinruecken, G., Beckers, R. (Eds.), Cercospora beticola Sacc. Biology, Agronomic Influence and Control Measures in Sugar Beet, Vol. 2. International Institute for Beet Research, Brussels, pp. 49–76. Sch.aufele, W.R., Wevers, J.D.A., 1996. Possible contribution of tolerant and partially resistant sugar beet varieties to the control of the foliar disease Cercospora beticola. Proceedings of the 59th IIRB Congress, Brussels, Belgium, pp. 19–32. Shane, W.W., Teng, P.S., 1992. Impact of Cercospora leaf spot on root weight, sugar yield, and purity of Beta vulgaris. Plant Dis. 76, 812–820. Skaracis, G.N., Biancardi, E., 2000. Breeding for Cercospora resistance in sugar beet. In: Asher, M.J.C., Holtschulte, B., Richard Molard, M., Rosso, F., Steinruecken, G., Beckers, R. (Eds.), Cercospora beticola Sacc. Biology, Agronomic Influence and Control Measures in Sugar Beet, Vol. 2. International Institute for Beet Research, Brussels, pp. 177–195. Skaracis, G.N., Ioannidis, P.M., Ioannidis, P.I., 1996. Integrated management systems against sugarbeet Cercospora leaf spot
J. Vereijssen et al. / Crop Protection 22 (2003) 201–209 disease. Proceedings of the 59th IIRB Congress, Brussels, Belgium, pp. 45–54. Verreet, J.-A., Wolf, P., Weis, F.-J., 1996. Bek.ampfungsschwellen als Grundlage fur . eine integrierte Bek.ampfung von Cercospora beticola—Das IPS Modell Zuckerrube, . Proceedings of the 59th IIRB Congress, Brussels, Belgium, pp. 55–69. Windels, C.E., Lamey, H.A., Hilde, D., Widner, J., Knudsen, T., 1998. A Cercospora leaf spot model for sugar beet: in practice by an industry. Plant Dis. 82, 716–726. Wolf, P.F.J., Verreet, J.-A., 2002. An integrated pest management system in Germany for the control of fungal leaf diseases in sugar beet: the IPM sugar beet model. Plant Dis. 86, 336–344.
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Wolf, P.F.J., Weis, F.-J., Verreet, J.-A., Burcky, . K., Maier, J., Tischner, H., 1998. IPS (Integriertes Pflanzenschutzsystem)-Modell Zuckerr.ube—Entwicklungsschritte und Einfuhrung . in die Praxis. Gesunde Pflanz. 50, 264–272. Zadoks, J.C., Schein, R.D., 1979. Disease and crop loss assessment. In: Zadoks, J.C., Schein, R.D. (Eds.), Epidemiology and Plant Disease Management. Oxford University Press, Oxford, England, pp. 237–281.
Comparison of two disease assessment methods for assessing Cercospora leaf spot in sugar beet J. Vereijssena,*, J.H.M. Schneidera, A.J. Termorshuizenb, M.J. Jegerc b
a Institute of Sugar Beet Research, P.O. Box 32, 4600 AA, Bergen op Zoom, The Netherlands Biological Farming Systems, Wageningen University, Marijkeweg 22, 6709 PG, Wageningen, The Netherlands c Imperial College at Wye, Wye, Ashford, Kent TN25 5AH, UK
Received 18 December 2001; received in revised form 12 April 2002; accepted 8 August 2002
Abstract Two disease severity scales for Cercospora leaf spot (CLS) assessment were compared. CLS was assessed in two experimental fields in the Netherlands in 1999 using two scales: a single leaf severity assessment, as used in the IPS-system (Integriertes Pflanzenschutzsystem) in Germany, referred to as DSIPS, and a whole plant assessment, Agronomica diagram from Italy, referred to as DSAGR. To obtain a range of disease severities, fungicides were applied at defined action thresholds based on disease incidence and severity. There was an exponential relationship between DSIPS and DSAGR (R2 ¼ 86%) for pooled data with little change in the whole plant assessment above DSIPS=5%. An exponential curve best fitted DSIPS and root and sugar weight in fields severely infected with CLS, whereas a linear curve was found for mildly infected fields. A linear curve fitted DSAGR best with root and sugar weight in both severely and mildly infected fields. No relationship was found between both DSIPS and DSAGR and sugar content. The use of DSAGR was less time consuming in monitoring and was done with greater accuracy, efficiency and level of reproducibility than DSIPS. These results demonstrate that CLS assessment can be less time-consuming and more practical in application. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Cercospora beticola; Disease assessment method; Sugar beet; Disease scale
1. Introduction Cercospora leaf spot (CLS) in sugar beet (Beta vulgaris L.) caused by Cercospora beticola Sacc. occurs world-wide and may cause a reduction of 42% gross sugar yield (Shane and Teng, 1992) which leads to problems (less extractable sugar) at the sugar factory and less income for farmers. CLS decreases both beet root weight and sugar content, resulting in reduced gross sugar yield (root weight sugar content). In severe epidemics the foliage will be totally destroyed and the beet starts to grow new leaves; flushes of new growth. The effect of CLS severity on a sugar beet plant has been measured via the yield parameters root weight and sugar content in variety or fungicide application field trials (e.g. Shane and Teng, 1992). Root weight and sugar content are strong negatively influenced by the extent of *Corresponding author. Tel.: +31-164-274400; fax: +31-164250962. E-mail address: [email protected] (J. Vereijssen).
new growth. In the Netherlands, where the area infested with CLS has increased during the last 25 years to 34% of the cultivated sugar beet area (Holtschulte, 2000), losses of up to 20% in gross sugar yield due to CLS have been recorded (Vereijssen, unpublished data). Resistance to CLS is only partial (Skaracis and Biancardi, 2000), and not widely available in commercial varieties, so does not replace the need for a fungicide treatment. Two fungicides, with benomyl or carbendazim (both benzimidazoles) as the active ingredient, are registered to control CLS in the Netherlands, but resistance to these fungicides has been reported in the south-eastern part of the country (Sch.aufele and Wevers, 1996). At present, fungicide treatment according to a fixed schedule offers good control of CLS, but this involves application of significant quantities of fungicide, even under conditions unfavourable for any increase in disease (e.g. dry weather). To meet government policy objectives the use of fungicides has to be reduced in the Netherlands (Anon., 1991), and rate of approval of new fungicides has slowed down in recent years.
0261-2194/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 1 - 2 1 9 4 ( 0 2 ) 0 0 1 4 6 - 1
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Durable and profitable control of CLS could be obtained if fungicides are applied according to action thresholds; this means applying a fungicide when the incidence or severity of disease exceeds the amount of disease that can be tolerated in the crop. Using action thresholds, fungicide use can be decreased in sugar beet. To develop action thresholds and judge their effectiveness in disease control the amount of disease must be quantified. Without such data, estimation of disease progress rates, comparison of treatments such as varieties or control measures, and prediction of future disease or yield loss is not possible (O’Brien and Van Bruggen, 1992). Quantifying the amount of disease can also lead to fungicide reduction and determining the amount of disease that can be tolerated in the field (Zadoks and Schein, 1979). According to Kranz (1990), the best assessment system is one based on standard diagrams or keys that illustrate what the evaluator should see for each classification in a continuous disease scale. A powdery mildew standard diagram has been described in sugar beet (Hills et al., 1980), and several CLS single leaf standard diagrams have been proposed (for example Anon., 1970; Rossi and Battilani, 1989; Battilani et al., 1990). The Kleinwanzlebener Saatzucht (KWS) scale (Anon., 1970) for CLS is described for both
single leaves and whole plants. Shane and Teng (1992), however, concluded that the KWS scale was unsuitable for accurate yield loss studies. The KWS scale categories are not precisely defined, especially at low disease intensities and the scale is more appropriate for evaluation of breeding lines in inoculated trials where there is a need for rapid assessment of plants with relatively high severities (Shane and Teng, 1992). Systems developed for control of CLS have used several assessment methods. Verreet et al. (1996) assessed severity on every single leaf of a beet plant and calculated whole plant severity from these data. Windels et al. (1998) assessed severity according to a Spot Percentage scale (Jones and Windels, 1991) on the older, lower leaves of 100 plants in a field. Some authors do not mention the scales or standard diagrams used (Rossi and Battilani, 1991; Rossi et al., 1994; Skaracis et al., 1996). The Agronomica diagram (Fig. 1) is commonly used in the field in Italy, and its values are subsequently used to recalculate severity for use in simulation models as a basis for a computer-based decision support system (Merrigi, pers. comm.) to forecast both disease progress rates (Rossi et al., 1994) and primary infections (Rossi and Battilani, 1991). Rossberg et al. (2000) used 50% prevalence of fields as a threshold in a district in their
Fig. 1. Agronomica diagram for disease assessment of Cercospora leaf spot on sugar beet.
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model. This corresponds to roughly 5% diseased leaves in a field and indicates the timing of a preventive fungicide application, which is currently used in Germany (Wolf et al., 1998; Rossberg et al., 2000). In our study we compared two existing disease severity assessment scales, and determined their practical value for CLS disease assessment in the Netherlands: the single leaf severity scale from Germany, used in the IPS-system (Integriertes Pflanzenschutzsystem) (Verreet et al., 1996), further referred to as DSIPS and the Agronomica whole plant diagram or field key from Italy (Battilani et al., 1990) (Fig. 1), DSAGR. DSIPS was chosen, as it has proven successful in Germany (Wolf et al., 1998). DSAGR is used extensively in Italy to forecast disease progress (Rossi et al., 1994) and rationalise fungicide use (Rossi and Battilani, 1991). Data must be accurate and precise for evaluating the extent of control by action thresholds, and its collection must be efficient in terms of time and costs. DSIPS is laborious but the quantity and quality of information is sufficient for development of action thresholds (Verreet et al., 1996). However, this method of assessment can be quite destructive within a crop due through the snapping off of leaf petioles when assessing severity. Also feeding of rabbits on sugar beet leaves can be disastrous for the assessment, as whole leaves can disappear. The use of ‘‘whole-plant’’ diagrams (Kranz, 1990) or field keys (Large, 1966) such as DSAGR, by contrast, is less time consuming, and improves efficiency, reproducibility and accuracy for both experienced and inexperienced evaluators (Kranz, 1990). Such diagrams must include sufficient intervals to represent all stages of disease development (O’Brien and van Bruggen, 1992). A ‘‘whole-plant’’ diagram removes the need for an evaluator to make assessments at the whole-plant level from diagrams constructed for individual leaves (Kranz, 1990). Our research aims to develop a decision support system for CLS in sugar beet in the Netherlands. A primary objective is to make CLS disease assessment less time consuming and more practical in application than DSIPS, but equally reliable and reproducible. This paper describes (i) a comparison of DSAGR with DSIPS in assessment of CLS, (ii) the relation between DSIPS and root weight, sugar content and sugar weight, and (iii) the relation between DSAGR and the same yield variables. These findings will subsequently be incorporate in a DSS for the Netherlands.
2. Materials and methods 2.1. Experimental fields Field trials were conducted at two locations in the Netherlands in 1999: at Maasbree (in the province of
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Limburg) and at Toldijk (in the province of Gelderland). The province of Limburg has a long CLS history with severe epidemics. The province of Gelderland is a relatively new CLS area with less severe epidemics. These fields, expected to differ in CLS severity, were intentionally chosen to determine the performance of each scale at both low and high severities. Farmer’s fields were drilled with Beta vulgaris: cv. Tiara at Maasbree (sown April 10) and cv. Rebecca at Toldijk (sown March 29). The experimental design was 6 treatments (4 fungicide sprays, untreated and calendar sprayed), to obtain a range of CLS severities, in randomised blocks with four replications. Experimental units were 3 m (6 rows of sugar beet) 12.5 m and blocks were separated by 2.5 m bare soil. Beets were harvested manually on November 3 at Maasbree and September 28 at Toldijk, according to fixed beet delivery contracts between the farmers and the sugar industry. Only the inner four rows of each plot were harvested. Root yields and sugar contents were analysed at the IRS using a standard protocol (de Bruijn et al., 1999).
2.2. Disease assessment At Maasbree, plants were monitored weekly from July 27 to September 13, and at Toldijk from July 29 to September 23. For each experimental unit (plot) of 3 m 12.5 m, five plants (3 in row 2, 2 in row 5) (20 plants per treatment) were used for severity assessment. Five plants were monitored due to time restrictions, and one person carried out the assessments. CLS severity was assessed using two methods: DSIPS and DSAGR. DSIPS, as used in the IPS-system (Verreet et al., 1996), is a single-leaf severity assessment based on pre-defined levels of leaf area diseased (0%; 0.1%; 0.3%; 0.5%; 1%; 5%; 10%; 25%; 50%; 75%; 90%; 100%) for each leaf on a plant. For DSIPS the first three green petioles were tagged with different coloured rings to assist monitoring. As the first green leaf died, rings were moved anticlockwise following the rosette until the next green petiole was marked. Severity of all leaves on a plant was assessed and data were recorded directly in a laptop computer. Severity of all leaves was subsequently expressed as the mean per plant, resulting in a wholeplant severity number. The average whole-plant severity of 20 plants per treatment per week is used for the action thresholds. DSAGR, Agronomica diagram (Battilani et al., 1990), is a whole plant assessment based on 11 classes (0–5) from healthy through to totally destroyed foliage (Fig. 1). When DSAGR=5, the value 0.5 is added to the scale for every additional week to take into account sugar losses due to growth flushes. The average DSAGR value of 20 plants per treatment per week is calculated and used as the average disease index for a treatment.
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2.3. Treatments
4. Results
Four different fungicide treatments were applied to obtain a range of CLS severities; an unsprayed control and calendar sprayed treatment were also included. The unsprayed control was assumed to give the maximum CLS severity for a field and thus the lowest yield, whereas the calendar spraying was expected to give the maximum yield. A triazole fungicide (a.i. difenoconazole 250 g/l) was applied (0.4 l/ha at 3.3 bar) at both locations using a hand-held horizontal spray boom with six Teejet 11003 vs nozzles 50 cm apart with 50 mesh anti-drop screen at a walking speed of 1 m/s.
4.1. Relation between DSIPS and DSAGR
3. Statistical analysis 3.1. Relation between DSIPS and DSAGR To obtain a range of disease from low to high severities, severity data for the six treatments were pooled for each location. Disease severities of all assessment weeks, according to DSIPS and DSAGR at the plant level, were used to compare the two assessment scales per location. The relationship between DSIPS and DSAGR was described using nonlinear regression (Genstat, Version 3.3.4). Equality of the asymptotes of the regression functions at each location were tested with a t-test. Also, severity data according to DSAGR and DSIPS of both locations were pooled and a general relationship between DSIPS and DSAGR was described using nonlinear regression (Genstat, Version 3.3.4). 3.2. Relationship between disease severity assessment and yield parameters Mean disease severity at the plot level assessed using DSIPS and DSAGR was used as the independent variable in regression analyses with root weight, sugar content and sugar weight (root weight sugar content), as the dependent variable. To account for variety and location differences, relative values of the yield variables were used in the statistical analyses. Mean root weight, sugar content and sugar weight of the calendar sprayed treatment were set at 100 for each field with values for the other treatments calculated accordingly. Root weights, sugar contents and sugar weights were regressed on disease severities using Genstat procedures. The best fitting curve was determined based firstly on homogeneity of the error variance in the residual plot and secondly on variance accounted for (R2 ) by the model. The significance of the regression coefficient, deviance from zero in either the positive or negative sense, was tested using a two-sided t-test.
As expected a minor CLS epidemic (low severities) developed at Toldijk and a major epidemic (high severities) at Maasbree (Fig. 2), and within each field, treatments also affected CLS severity. For the major epidemic in Maasbree, high disease levels on some plants (DSAGR=5) were accompanied by flushes of growth. In this situation, 3–4 times more leaves per plant per week can be produced in comparison with plants with a lower level of disease, with presumably the plant investing energy in leaf rather than sugar production. An exponential model best described the relationship between DSIPS and DSAGR at Maasbree and Toldijk (Fig. 3), with little change in DSAGR above DSIPS values above 5%. Although transformation of DSIPS (arcsine, square root, logit) did increase the variance accounted for (R2 ), the residual plots showed heteroscedasticity (data not shown). A homogeneous pattern of residuals was obtained with untransformed data. The variance accounted for (R2 ) was high for both Maasbree (86%) and Toldijk (88%), and for pooled data (86%) (Fig. 3). The exponential curve for pooled data had an asymptote at DSAGR=4.3, indicating the highest average DSAGR value across both locations. The asymptote of the exponential curve for the major epidemic at Maasbree (DSAGR=4.5) was significantly higher than for the minor epidemic at Toldijk (DSAGR=3.7) (P > 0:05) (Fig. 3). Because CLS severity was lower in Toldijk (Fig. 3), the fitted curve was estimated from severities less than 15%, whereas for Maasbree there were also many data points in the range 15–32% (Fig. 3). In the linear part of the curve (DSIPS=0– 2.5%) the fitted curve for pooled data gave a good fit to both data sets (Fig. 3). 4.2. Relationship between DSIPS and yield parameters At Maasbree there were exponential relationships between relative root weight (Fig. 4a, 100%=87.0 t/ha) and relative sugar weight (Fig. 4c, 100%=13.8 t/ha) with severities assessed according to DSIPS. Corresponding R2 values were 43% and 50%, respectively. At Toldijk, with only a minor CLS epidemic, the linear relationship between relative root weight and DSIPS (R2 ¼ 0:3%) was not significant (b ¼ 0:5; P ¼ 0:5) (Fig. 4a, 100%=81.9 t/ha). For relative sugar weight a significant linear relationship (R2 ¼ 16%) was observed (b ¼ 1:0; P ¼ 0:05) (Fig. 4c, 100%=13.4 t/ha). Transformation of DSIPS data (arcsine, square root, logit) did not improve homogeneity of the residuals, sometimes it increased variance accounted for (R2 ) to a maximum value of 0.9% for relative root weight and 20.1% for
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18 16
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33 35 37 39 assessment week Fig. 2. Disease progress curves of Cercospora leaf spot of unsprayed control treatment at Maasbree and at Toldijk 1999. Points represent means of four replicates of each 5 plants; bars at week 38 are standard errors.
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Fig. 3. Comparison of DSIPS and DSAGR at Maasbree and at Toldijk in 1999. Pooled data: y ¼ 4:3ð70:05Þ 3:7ð70:05Þn 0:4ð70:01Þx (regression line given), R2 ¼ 86; Maasbree (&): y ¼ 4:5ð70:06Þ 3:9ð70:06Þn 0:5ð70:01Þx ; R2 ¼ 86; Toldijk (’): y ¼ 3:7ð70:06Þ 3:3ð70:06Þn 0:2ð70:015Þx ; R2 ¼ 88: Standard errors are in parentheses.
relative sugar weight. At both locations relative sugar weight decreased more rapidly with increasing severity than relative root weight. There were no statistically significant relationships between relative sugar content and DSIPS (data not shown).
4.3. Relationship between DSAGR and yield parameters At Maasbree, significant linear relationships between relative root weight (b ¼ 3:6; P ¼ 0:005) (Fig. 4b) and relative sugar weight (b ¼ 5:7; P ¼ 0:001) (Fig. 4d) and
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Fig. 4. Relation between DSIPS and DSAGR and yield variables at Maasbree and at Toldijk in 1999. (A) DSIPS and relative root weight, Maasbree (&): y ¼ 91:1ð72:23Þ þ 14:1ð74:26Þn 0:6ð70:21Þx ; R2 ¼ 43 and Toldijk (’): y ¼ 100:6ð71:12Þ 0:5ð70:53Þx; R2 ¼ 0:3: (B) DSAGR and relative root weight, Maasbree (&): y ¼ 3:6ð70:95Þx þ 109:0ð73:35Þ; R2 ¼ 42 and Toldijk (’): y ¼ 1:6ð70:82Þx þ 104:2ð72:39Þ; R2 ¼ 11: (C) DSIPS and relative sugar weight, Maasbree (&): y ¼ 84:4ð72:78Þ þ 23:3ð77:24Þn 0:5ð70:21Þx ; R2 ¼ 50 and Toldijk (’): y ¼ 99:4ð70:90Þ 1:0ð70:42Þx; R2 ¼ 16: (D) DSAGR and relative sugar weight, Maasbree (&): y ¼ 5:7ð71:20Þx þ 111:5ð74:23Þ; R2 ¼ 55 and Toldijk (’): y ¼ 1:9ð70:65Þx þ103:4ð71:89Þ; R2 ¼ 27: Standard errors are in parentheses. Maasbree: root weight 100%=87.0 t/ha, sugar weight 100%=13.8 t/ha; Toldijk root weight 100%=81.9 t/ha, sugar weight 100%=13.4 t/ha.
DSAGR were observed. At Toldijk, the linear relationship between relative root weight and DSAGR was not significant (b ¼ 1:6; P ¼ 0:1), but a significant linear relationship was found for relative sugar weight (b ¼ 1:9; P ¼ 0:01). Although all three yield variables decreased as severity increased, the regression with relative sugar weight as the dependent variable had the highest percentage variances accounted for (R2 ¼ 55% at Maasbree and R2 ¼ 27% at Toldijk). For relative root weight R2 values of 42% at Maasbree and 11% at Toldijk were obtained. Transformation of DSAGR data did neither improve homogeneity of the residuals nor increase R2 :
5. Discussion 5.1. Relationship between DSIPS and DSAGR In the Netherlands, the exponential relationships between severity assessed according to DSIPS and
DSAGR were similar to those when developing the Agronomica diagram in Italy (Rossi, pers. comm.). CLS is more severe in Italy, therefore the asymptote of the exponential curve DSAGR=4.8 was higher than the DSAGR=4.3 from our pooled data. The difference in asymptote means a jump in the Agronomica diagram from 4.5 to 5 (Fig. 1), indicating a greater preponderance of growth. The main use of the DSIPS assessments in this study is to calculate whether an action threshold will be reached in the field. The relationship between DSIPS and DSAGR can now be used to resolve the DSAGR value corresponding to a given severity value. Consequently, the action thresholds, expressed in severity values, as defined by Verreet et al. (1996) and used in this study, can be translated into DSAGR values. The calculations are not affected by the asymptote of the exponential curve, as the action thresholds based on DSIPS all are situated in the linear portion of the curve (DSIPSo5%). Future research will concern use and development of action thresholds based on DSAGR values.
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5.2. Relationship between disease severity assessment and yield parameters Although we obtained low R2 values, the regression coefficient was significantly different from 0, indicating a significant linear association between yield variable and assessment key. The purpose of relating the yield variables to the assessment keys was to indicate the differences between the two keys. A problem with disease assessments in newly affected CLS areas (Toldijk) in contrast to areas with a previous history of CLS (Maasbree) is the patchy occurrence of heavily diseased individual plants. Relatively healthy plants can be found surrounding plants at DSAGR=4 (Vereijssen, unpublished data). It is evident that this will have an influence on the calculation of whole plot (experimental unit) severity, and subsequently on treatment severity. The low variance accounted for in Toldijk may be due to no account being taken of the aggregation of diseased plants. Kranz (1987) has noted that the sampling technique is influenced by the spatial distribution of a disease in a crop. He recommends in most cases a systematic sampling (e.g. ‘‘W ’’—pattern with x sampling units at y metres distant along the path). However, for decision making in disease control sequential sampling is often appropriate (Kranz, 1987). The spatial variation of CLS diseased plants will be dealt with in a future paper. The difference between the two sites in yield at the same disease level could also be a result of varietal difference, not only in maximum yield, but also in susceptibility, which may be influenced by local environmental factors. Although both varieties are susceptible to CLS, there may be quantitative differences in sensitivity. Also the difference in soil type between the locations may cause differences in yield, either directly or through interactions with variety. We have shown that root weight, sugar content and sugar weight decrease exponentially in heavily diseased fields and linearly in mildly diseased fields when using DSIPS. Wolf et al. (1998), however, described a linear relationship between DSIPS and sugar yield loss in Germany with severity levels as high as 60% (see also Wolf and Verreet, 2002). According to Kranz (1990) this is quite a high level as leaf spot diseases in general rarely exceed 37% severity. From our observations, an assessment of 30% severity at the plant level gives a beet plant without green leaves except for those showing flushes of new growth. An advantage of DSAGR is that a linear model describes the relationship between DSAGR and yield loss in both minor and major epidemics. The linear association between both relative root and sugar weight and DSAGR was most pronounced in the major epidemics at Maasbree, as CLS will have a stronger effect on these variables. The strongest linear association, at both locations, is between relative sugar weight
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and DSAGR, indicating a combined action of both root weight and sugar content on yield loss. Another advantage of DSAGR is that it stretches severity in the 0–5 range, ignoring the 0.5 increment, for both locations whereas with DSIPS, data points are clustered within a small range. DSAGR, although simply constructed is easy to use and more reliable in predicting yield loss than the detailed and labour intensive DSIPS. 5.3. Interpretation and implementation for disease assessment The relationship between DSIPS and root weight, sugar content and sugar weight in a heavily diseased field was linear up to a DSIPS value of about 5%. The 5% severity level corresponds to a DSAGR between 4 and 4.5 (Fig. 2). This is almost at the stage of DSAGR where 0.5 is added to the score weekly, and this might explain why yield variables are linearly related to DSAGR. When beet produces new leaves as a response to totally destroyed foliage, whole-plant severity levels off and even decreases. As many as 12 new leaves per week can be grown during flushes of new growth compared to 3–4 leaves previously in the season. Jensen and Boyle (1966) already observed decreasing severity for C. arachidicola on peanut: ‘‘if the amount of disease is expressed as a percentage, a decrease in the amount of infection could occur at a later sampling date during periods of extremely rapid growth if only a few new leaves showed disease symptoms’’. This did not occur in our study, however, as although there were some flushes of growth, severity increased also on older alreadydiseased leaves. Wolf and Verreet (2002) did not indicate what happened with severity using DSIPS when high severities were reached, although they defined a tolerance limit for disease severity at harvest in their yieldloss relationships. Severity at harvest time is underestimated in their case, which will evidently lead to incorrect relationships between yield and severity. Also the maximum number of leaves grown per week was set at a maximum of 8 in DSIPS, irrespective of the stage of growth. This is a flaw of the system as more leaves can be grown in a week during flushes of growth (Rossi et al., 2000). DSIPS can thus not be easily used in Greece and Italy where CLS epidemics are generally more severe than in the Netherlands. Decreasing severity is not a problem from a practical point of view, e.g. when applying fungicides, but in model development (e.g. disease progress modelling) underestimation of disease severity can be a serious problem. DSAGR, however, continues to increase in severity as with totally destroyed foliage (DSAGR=5), 0.5 is added to the scale weekly. Regarding the effect of CLS on yield variables, Rossi et al. (2000) remarked that ‘‘flushes of growth is certainly thought to be at least in part responsible for the lowering of the sugar content and, more signifi-
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cantly, for the drop in processing quality that frequently occur in the late growing season’’, which argues for the use of DSAGR in yield loss assessments. When choosing between the two assessments scales precision, accuracy and reproducibility must be dealt with, as well as the time and cost involved. An advantage of DSIPS over DSAGR is that a distinction can be made between CLS necrosis and normally necrotic tissue. However, errors are made in estimating severity, as evaluators generally tend to overestimate (low disease levels) or underestimate (high disease levels) severity using a percentage scale (Kranz, 1987). Per plant an estimation error can be made 50–80 times, depending on the number of leaves per plant, which affects the accuracy of disease assessment, although some errors may cancel themselves out at the whole plant level. As three-dimensional entities such as the whole-plant are particularly difficult to estimate, Kranz (1987) proposed that the smallest possible sampling unit, such as a leaf or fruit, should be used to increase precision of the disease assessment. The accuracy and reproducibility of assessments is higher for DSAGR as the diagram has an accompanying text, which explains what the evaluator should see. The variation between evaluators should be smaller than using DSIPS. However, for both DSIPS and DSAGR training of new evaluators is needed. DSAGR can be vulnerable to errors compared to DSIPS when monitoring in bright sunlight, on wet plants or on darker coloured varieties. There is always some averaging when a diagram is used, or as Large (1966) concluded, ‘‘no standard diagram can show all differing distributions that can make up any given percentage of cover’’. This is also true for DSAGR, as the observer has to average CLS over the whole plant before assigning a DSAGR score to a plant, even with additional text to supplement the diagram. However, disease assessment using DSAGR is faster, thus less costly and less destructive to the plants than using DSIPS. This study has formed a basis for a simplified disease assessment in the development of a decision support system for CLS in the Netherlands. Use of DSAGR provides less time-consuming monitoring and also greater accuracy, efficiency and reproducibility. We anticipate that extension workers in the sugar industries and researchers in future Cercospora research can use the Agronomica diagram for field monitoring.
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