Ann. appl. Biol. (1986), 109, 523-534 Printed in Great Britain

523

Epidemics of Phymatotrichum root rot (Phymatotrichum omnivorum) in cotton: environmental correlates of final incidence

and forecasting criteria BY M. J . JEGER* AND S. D. LYDA

Department of Plant Pathology and Microbiology, Texas Agricultural Experiment Station, The Texas A & M University System, College Station, TX 77843, USA

(Accepted 14 April 1986)

SUMMARY

Epidemics of Phymatotrichum root rot (PRR), caused by Phymatotrichum omniuorum, in cotton were monitored in field plots at the Blackland Research Center, Temple, Texas during the years 1969-1982. In most years disease incidence, assessed at periodic intervals during the growing season, increased smoothly and levelled off at values in the range 0.04-0.99 (proportion of plants killed). During two years there were marked increases in PRR late in the growing season. Increments in PRR were associated with preceding increments in precipitation but only when the latter were large. The final incidence of PRR late in August was directly related (in all but one of the 14 years) to cumulative precipitation in the range 36-100 cm, and inversely related (in all 14 years) to air temperatures greater than 34°C. Regression was used to relate final PRR incidence to derived weather variates but the equations obtained did not provide a realistic basis for forecasting : the best predictive variates included cumulative precipitation up to mid-August. A threshold criterion given by PIT > 1.45, where P was cumulative precipitation (cm) from 1 January and T was the mean maximum temperature (“C) during the preceding 10 days, was derived. This criterion satisfactorily grouped years with high (> 0-50) and low ( < 0.50) incidences of PRR. In those years where a forecast of high incidence was made, the criterion was satisfied prior to the first appearance of PRR (mid-June); where a forecast of low incidence was made, the criterion was not satisfied until late August. The forecasting criterion was evaluated at three sites in the Blacklands region in 1983, at two in 1984, and at Temple only in 1985. In 1983 and 1984, final PRR incidences of less than 0.50 were forecast and observed; in 1985, cumulative annual precipitation exceeded 50 cm by early June, the threshold criterion was satisfied prior to the first observation of symptoms, and PRR incidence was 0.80 late in August.

INTRODUCTION

Phymatotrichum omnivorum Dug. [ Phymatotrichopsis omniuora (Dug.) Henneb.] is a soil- borne fungal pathogen causing Phymatotrichum root rot (PRR) in many economic crops in the restricted geographical area of southwestern USA and northern Mexico. Details of the biology, host range, symptomatology, ecology, and geographical range of P. omniuorum have been reviewed (Streets & Bloss, 1973 ; Lyda, 1978; Percy, 1983). Of special importance are the very wide host range ( - 2000 species, mainly of dicotyledonous plants), no evidence for constitutive resistance in any major economic host, and the overwintering of sclerotia deep within the soil profile, a combination of factors that makes control of PRR a difficult

* Present address: Tropical Development and Research Institute, 56-62 Gray’s Inn Road, London WClX 8LU 0 1986 Association of Applied Biologists

524 M. J . JEGER A N D S . D . L Y D A

proposition in areas favourable to the pathogen. The distribution of the fungus in North America was recently reviewed by Percy (1983) and is closely linked to a combination of edaphic, environmental, and epidemiological factors.

The Blackland region of north central Texas extends for about 5 million hectares and is heavily infested with P. omniuorum. The soil type is an alkaline clay vertisol (Udic Pellusterts [fine, montmorillonitic, thermic]) considered especially conductive to the growth of the fungus. Cotton production in this region of Texas has been severely curtailed by the presence of P . omnivorum, although production of non-irrigated cotton is exceeded only in the high and low plains of northwestern Texas (Anon., 1984). Despite the potentially high loss due to the disease, the appearance of the disease is often localised within portions of individual farms, and many growers continue to grow cotton in the Blackland region.

Apart from the avoidance of infested areas, PRR has proved one of the most difficult of plant diseases to control (Streets & Bloss, 1973). Management options with respect to PRR, at present, are more of a strategic rather than of a tactical nature and involve crop rotation, the choice of early maturing cultivars, organic and inorganic amendments to soil, or practices such as deep chisel ploughing known to have a beneficial effect probably through stimulation of antagonistic microflora and destruction of fungal strands in the soil. Each of these cultural practises has on occasion provided some level of control, but no one approach has consistently proved effective and economic. The occurrence of overwintering structures deep in the soil has been an insuparable barrier to economic fungicide or fumigant treatments. The development of basipetally-translocated systemic fungicides may offer new opportunities for control.

Despite the lack of control options there would be a benefit in a weather-based forecasting scheme to alert farmers to the potential losses from PRR that may result in a given year. The dependence of PRR on environmental factors, especially precipitation, has been noted on many occasions (eg. Taubenhaus & Dana, 1929; Ratliffe, 1934; Ezekiel, 1938; Rogers, 1942). although few quantitative relationships have been reported. More detailed studies of soil environment factors, such as p H , aeration, and temperature, have been made (Lyda, 1978), but the effect of soil moisture on PRR epidemiology has not been reported in detail. The main aim of the analyses described in this paper was to examine the feasibility of developing forecasting criteria for PRR in the Blackland region of north central Texas and the impact of those criteria on the decision-making process within integrated pest management (IPM) programs for cotton (El-Zik & Frisbie, 1985). The occurrence of PRR at a particular location, and demonstrated crop mortality of 100% in some fields (Lyda, 1978), need to be taken into account when planning management practices for cotton production in the Blackland region. Previously, Ezekiel (1938) developed a predictive equation for PRR from data collected in the Blackland region in 1937. To our knowledge this was the first reported use of multiple regression analysis in plant pathology and certainly pre-dates any study cited by Butt & Royle (1 974).

MATERIALS A N D METHODS

Data on the seasonal development of PRR epidemics at the Blackland Research Center were collected by the second author annually from 1969-1982. One field at the Blackland Research Center of the Texas Agricultural Experiment Station, Temple, Texas, was used for all observations. Replicated plots (ranging from 3-5) were planted in sizes up to 40 rows x 60 m row length. Generally, two adjacent rows of either 30 or 60 m, and spaced 1 m apart, were randomly selected in each plot and all plants in the rows were assessed. In 1981 and 1982 more rows (four and six respectively) of shorter length (15 m) were assessed. Cultivars planted were Lankart 57, 3048, and LX-571 (1969-1971, 1978); Tamcot SP37 (1973-1977); and GP37-74 (1978-1982). Planting date ranged from 7 April (1978) to 1 June (1977) with a median date of

Forecasting Phymatotrichum root rot incidence in cotton 525

26 April (1982). The standard (at that time) Station management practices of ground preparation, planting, weed control, and pesticide application, were followed in each year. In most years the cotton was shredded rather than harvested and yield data were not collected. Yield loss due to PRR was estimated using the method of Ezekiel & Taubenhaus (Streets & Bloss, 1973). This method ascribes loss to the proportion of plants dead 7 wk before harvest plus the proportion dying in the next 4 wk. Precipitation and maximum and minimum air temperatures were recorded at a National Weather Service station located less than 0-5 km from the experimental field. Disease incidence (proportion of plants with PRR symptoms) was assessed at periodic intervals, ranging from four to 12 assessments, during each growing season. The disease symptom used in assessment was the initial wilting of upper leaves : this symptom is rapidly followed by wilting of the lower leaves and plant death (Streets & Bloss, 1973). All plants in the assessed plots were recorded on each occasion.

Data were analysed in three stages: I), visual examination of disease progress curves to determine general characteristics of epidemics over the 14-yr period; 2), evaluation of the relationship between increments in PRR and patterns of precipitation and air temperature during different periods of the calendar year; and 3), determining the relationship between final incidence of PRR and measured and derived weather variates using multiple regression analysis. Evaluations of regression equations and other forecasting criteria were made with historical data of PRR incidence and weather reported by Taubenhaus & Dana (1928) and Ratliffe (1934), and data since collected by Gerik, Rush & Jeger (1985), Jeger, Kenerley & Gerik (1985), and Koch, Jeger & Gerik (unpublished).

RESULTS

Epidemic progress and weather in 1969-1982 Disease progress curves representing the proportion of plants with PRR symptoms were

prepared from the means of replicated data for each of the 14 years. In all but two of the years disease incidence increased smoothly, although levelling off at different incidences. Disease progress curves are shown in Fig. 1 for years in which final incidence was less than 0-50 and in Fig. 2 for years in which final incidence was greater than 0-50. In 1971 (Fig. 1) and 1977 (Fig.

Table 1. Summary of temperature and precipitation data at the Blackland Research Center, Temple, Texas for the growing seasons in 1969-1982

Temperature days above

Precipitation (cm) b r > Cumulative 32 "C 38 "C

April May June July to 31 August ( 1 May-31 August)

I969 1970 1971 I972 1973 1974 1975 1976 1977 1978 1979 I980 1981 1982

11.0 8.7 4.9 4.5 6.4 6. I 2.3

24.6 21.3 6.5

14.6 8.7 8.9 9.5

2.2 11.2 9.6 7. I 7.6 6.2

21.5 11.4 7. I 5.7

24.0 18.0 8.4

12.0

I .9 I .o I .5 8.0

10.2 2.2

20.8 7.3 5.9

10.0 13.5 2.4

43.3 5.4

0.8 0.7 9.3 5.8 8.0 2.0

10.5 7. I 0 1.7

13.9 0 4.2 I .2

42.7 43.9 56.6 37.9 57.9 37.3 85. I 63.8 56.9 46.5

101.6 46.5

106. I 38.9

84 79 79 79 71 78 66 66 91 96 55 79 64 72

29 12 16

I 1

12 3 4

14 16 0

12 I 4

5 26 M . J . J E G E R AND S. D . L Y D A

I970 , 0 I50 2 10 270

I SO 210 270

Julian day

I50 210 270

Fig. I . Progress of Phymatotrichum root rot epidemics at the Blackland Research Center, Temple in years for which final incidence was less than y = 0.50.

1972 I 0 1976

f I , , .

0 210 170

SO 210 270 I50 210 270 I50 210 270

Jul ian day

Fig. 2. Progress of Phymatotrichum root rot epidemics at the Blackland Research Center, Temple in years for which final incidence was greater than y = 0.50.

2) there were marked increases in incidence late in the growing season. In the latter case, the planting date of 1 June was much later than in other years. The earliest that PRR was observed in the field was on Julian Day (JD) 163 (early June). The earliest that disease

Forecasting Phymatotrichum root rot incidence in cotton 527

assessments were concluded was on JD 232 (late August). The distributions of precipitation by calendar month during the growing season, and cumulatively to the end of August, are shown in Table 1. The temperature data have been summarised as numbers of days that air temperatures exceeded 32 and 38 "C during the growing season. These temperatures were chosen arbitrarily to represent average and extreme conditions, respectively, and do not represent cardinal values for pathogen activity.

Relationship of PRR incidence with weather The patterns of epidemic development were compared with the records of precipitation and

air temperature. The weather data were analysed in terms of Julian Days, usually at intervals of 10 days, rather than by calendar month. This interval of time corresponds to the more frequent disease assessments made in some years, and provides an average of weather conditions in this period of time.

The late increase in disease incidence observed in 1971 corresponded to a higher-than- average increment (26 cm) in precipitation during the period from JD 165 to JD 235. In general, however, increments in disease, expressed as absolute or relative increments, were poorly related to increments in precipitation during the epidemic, although the different timings and frequencies of disease assessments in the different years did not permit a rigorous evaluation of this relationship.

The next stage in the analysis was to relate the final incidence of PRR in each year to measured and derived environmental variates. One year, 1972, deviated markedly from the others in terms of the relationship between PRR and precipitation, and was omitted from the analyses; some possible reasons for this discrepant year are discussed later. In general, precipitation, accumulated from various dates in the current or previous calendar year and ending at various dates in the growing season, gave a good indication of the final incidence of PRR. The relationship was best when the measure of cumulative precipitation included amounts that fell late in the growing season. Fig. 3 shows the relationship between final PRR incidence ( y , logit transformed) and cumulative precipitation (P, log transformed) for the calendar year to JD 235. The relationship with precipitation at earlier dates, such as JD 165 or 205, was less satisfactory, The final incidence of PRR was related, negatively, to various measures of air temperatures during the growing season. Final incidence ( y , logit transformed) is plotted in Fig. 4 against the mean maximum air temperature (T, log transformed) calculated with respect to 10-day intervals during the period JD 165-235. The relationship was negative over the range observed (34-39 "C).

Development of forecasting criteria Regression analysis was used to quantify the influence of environmental variates on the

final level of PRR incidence. Incidence, the dependent variable, was transformed according to various transformations and regressed on In P , and In T as single independent variables, and on In P and In T as two independent variables in a multiple regression analysis. Transformations of the dependent variable included the complementary log-log transforma- tion In [ - h ( l - y ) ] and the logit transformation In [y/(l -y)]. A composite variable P/T was selected in the form of a quotient to constrain the influence of temperature in the observed range to be negative, as In (PIT) = In P - In T. The quotient P/Tcan also be interpreted as a crude 'supply' (precipitation) to 'demand' (air temperature) ratio. Regressions using the logit transformation gave better linear relationships than any other transformation in terms of significant coefficients, satisfactory patterns of residuals, and error mean square values, for any of the independent variable combinations and results are given in Table 2. The model using the quotient accounted for marginally more of the variance than the model for precipitation alone. The coefficient for the temperature variable in the multiple regression

528 M . J . JEGER A N D S. D . L Y D A

0

0 I 0-ni I 1 I 1 I 1

30 40 5 0 60 70 80 90 100 Cumulative precipitation. cm

(JD 235) Fig. 3. Relationship between final Phymatotrichum root rot incidence (y, logit transformed) and cumulative calendar precipitation (P, log transformed) to Julian day 235 (cm) at the Blackland Research Center, Temple for the years 1969-1982, excluding 1972.

0 1 0.99

0

34 35 36 37 38 39 Mean maximum temperature, "C

(JD 165 2.75)

Fig. 4. Relationship between final Phymatotrichum root rot incidence (y, logit transformed) and the mean maximum temperatures (T, log transformed) for the period Julian day 165-235 ("C based on 10-day intervals) at the Blackland Research Center, Temple for the years 1969-1982, excluding 1972.

Table 2. Regression coeficients (standard errors in parentheses) for equations in which logit ( y ) , where y represented Phymatotrichum root rot incidence, was regressed against: ( A ) In P and In T, as independent uariables in a multiple regression analysis; (B) In P; (C) In T; and

( D ) In (PIT), as single independent variables in linear regression % variance

In P In T In ( P l T ) accounted for

(A) 4.92 ( I ,498) -5.99 (15.395) 77.2 (B) 5.41 (0.796) 78.9 (C) -48.10 ( 1 1.712) 57.0 (Dl 5.00 (0.730) 79.3

Forecasting Phymatotrichum root rot incidence in cotton 529

was not significant at P = 0.05. The lines drawn in Figs 3 and 4 represent the fitted regression lines for precipitation and temperature respectively.

The regression analyses did not, however, result in equations that could be used in a practical forecasting scheme. The best independent variables were constructed with accumulated precipitation at JD 235. At this time severe epidemics would already have occurred. Cumulative precipitation at earlier stages of the growing season (e.g. JD 165 or JD 205) gave a less satisfactory relationship with final root rot incidence. A more pragmatic approach was then taken in which a severe epidemic was arbitrarily defined as one with a final incidence greater than 0.50: this incidence value corresponded to a PIT value of about 1.45 (cm/"C) and was evaluated as a threshold ratio of precipitation (supply) to temperature (demand). Weather data were re-examined from a threshold rather than continuous (regression) viewpoint. Cumulative precipitation at 10-day intervals was divided by the maximum temperature for the previous 10 days, and the earliest date that the threshold criterion was satisfied was noted for each year. In the 7 years (excluding 1972) that incidence exceeded 0.50 (Table 3), the threshold criterion was satisfied before Julian day 165, before the first appearance of above-ground symptoms. In the six years that incidence remained below 0.50, the threshold criterion was not satisfied until after JD 235, after the last disease assessments were made. Thus, during these 13 years, at least, the threshold criterion provided a discontinuity (70 days) on the basis of environmental conditions, and effectively separated years into those major (>0.50 incidence) or minor (C0.50 incidence) PRR epidemics. Estimates of average yield losses attributable to major and minor epidemics are given in Table 3.

Data were also examined to determine whether, over and above annual fluctuations, there were long term trends in PRR incidence. Final incidence (proportion), cumulative precipitation (cm), and mean maximum temperature ("C), expressed as deviations (+ or -)

Table 3. Relationship between the Julian day that the forecasting criterion* was satisfied, Phymatotrichum root rot development, and estimated yield loss? in years of ( A ) major (> 0.50

incidence) and (B) minor ( < 0.50 incidence) epidemics

Year Julian day (proportion) (%) Final disease level Estimated loss

(A ) 1976 I25 0.75 68 I977 I35 0.58 52 1979 I35 1 .oo 90 I980 I35 0.68 61 I975 145 0.9 1 82 I973 I55 0.65 59 1981 165(163)$ 0.79 - 71

70

(B) 1970 245 (235)(i 0.28 25 1971 245 0.32 29 1978 245 0.32 29 I974 255 0.04 4 I982 285 0.33 30 1969 295 0.22 - 20

22

mean loss for major epidemic

mean loss for minor epidemic

* Criterion is P/T > 1.45 where P i s cumulative precipitation (cm) and T is maximum air temperature ("C) during the previous 10 days.

Estimate of loss obtained using the procedure of Ezekiel and Taubenhaus (Streets & Bloss. 1973). $ Julian day on which root rot was first observed during 1969-1982. 5 Julian day on which disease assessments first ceased during 1969-1982.

530 M. J . J E G E R AND S. D. LYDA

Phvmatotrichum

, I 1 I I I I 68 1970 1972 1974 1976 1978 1980 1982

Cumulative A

I I I 58 1970 1972 1974 1976 1978 Id80 1982

I

Mean maximum temperature ("C) \

I 1 1 I I t

)8 1970 1972 1974 1976 1978 1980 1982 Year

Fig. 5. Deviations in final Phymatotrichum root rot incidence (proportion), cumulative precipitation (cm) to Julian day 235, and mean maximum temperatures ("C, based on 10-day intervals) during Julian days 165-235, for the years 1969-1982, including 1972. Values in parentheses represent the mean values for each variate over the 14 year period.

from their respective means during the period 1969-1982, are plotted in Fig. 5. Apart from the one year, 1972, there was good qualitative agreement in the pattern of deviations found : positive deviations in PRR incidence were associated with positive deviations in precipitation and negative deviations in temperatures, as would be expected from the preceding analyses. There was no evidence for an increasing trend in PRR over the 1Cyear period.

P R R incidence in 1972 In 1972, the pattern of deviation did not follow the trend shown in Fig. 5. Despite a less-

than-average cumulative precipitation for the calendar year there was a higher-than-average final incidence of PRR. The forecasting criterion developed above, when excluding the year 1972, would have failed to predict this high level of incidence. Data were re-examined for possible reasons for this discrepancy. The failure was due to the precipitation variable, as mean maximum temperatures were low (34 "C) in 1972. The range in difference between maximum and minimum temperatures was somewhat less than in other years, especially in late June (about 12 "C difference rather than an average of 16.5 "C), which was possibly indicative of cooler overcast conditions during which evapotranspiration was likely to have been low. Thus, despite a lower-than-average precipitation, the amount of available soil

Forecasting Phymatotrichum root rot incidence in cotton 53 1

water may have been higher than the precipitation records indicated. The prediction could have been made better by considering different starting points in the preceding year for accumulating precipitation, but following this procedure decreased the predictive ability for other years. No assessment of available soil water was available for the years 1969-1982.

Application of the Ezekiel (1938) equation The equation proposed by Ezekiel (1938) was y = - 168 + 1 5 . 5 ~ ~ + 2 0 . 5 ~ ~ + 30.8x3,

where y is PRR incidence (%, untransformed), and x l , x2, x3 are precipitations (inches) for April plus May, June, and July, respectively. This equation gave a multiple correlation coefficient of 0.86 when fitted for the data collected in 1937. That year was very dry, however, with maximum monthly precipitations of 2-9, 5.8, 12.9, and 8.3 cm for April, May, June and July, respectively, and incidences of root rot, assessed in late September, with a correspondingly low range of 0.03-0.29. Application of Ezekiel’s equation to the data reported here gave predicted PRR incidences that were sometimes negative (1969,1970, and 1974) or exceeded 100% (1975, 1976, 1979, and 1981). The Ezekiel equation did not predict the high relative level of incidence in 1972.

Evaluation of forecasting criteria The regression equation involving precipitation only was evaluated against data reported by

Taubenhaus & Dana (1929) for 1923-1927 and by Ratliffe (1934) for 1915-1932. The evaluation provided was limited in nature due to the very different husbandry, notably plant maturation, compared to more recent practices. In the former case no details on plot size or number of plots was provided, except that the total cotton population assessed comprised a representative portion of the cotton at the Temple Center. The incidences of PRR predicted from precipitation at 25 August and the observed incidences for each year 1923-1927 were: 0.66, 0.29; 0.37, 0.18; 0.01, 0.10; 0.75; 0.56; 0.73,0.22; where the first value of each pair was the predicted incidence. Although the equation made a reasonable prediction for the five year pattern of incidence, the observed incidence for any given year was much less than predicted except for 1925.

The data reported by Ratliffe (1934) were comprehensive and included 17 years’ data on PRR incidence in continuous cotton and in cotton in rotation with other crops at the southern extreme of the Blacklands region, 10 km south of San Antonio, Texas. There was a very complex design in time and space for these crop rotation and tillage experiments. There was also a considerable variation in disease in the continuously-grown cotton plots in most years that makes the use of mean plot values very problematical. Predicted incidences of PRR were again obtained with the regression equation involving precipitation only, using cumulative precipitation to the end of August. In 10 of the 17 years the predicted incidence fell within the range of plot values for continuously grown cotton; in four years the amount of PRR was underestimated, and in three years the amount was overestimated. To place these results in context, the mean amount of PRR in the cotton rotation plots fell within the range of values for continuously-grown cotton in 10 years, below the range in five years, and above the range in two years.

The forecasting criterion (PIT > 1.45) was used to evaluate PRR epidemics in 1983 (Gerik et al., 1985) and 1984 (Jeger et al., 1985), and 1985 (Koch, Jeger & Gerik, unpublished data) at different sites in the Blackland regions, including Temple, although meteorological data were only available from Temple. In 1983 the forecasting criterion was not satisfied until JD 220 and the epidemics at each site had levelled off at incidences less than 0.50 (mean value of 0-29) by 2-4 August. In 1984, the forecasting criterion was not satisfied until JD 283 and the epidemics had levelled off at a mean incidence of 0.35 by 13 August. In 1985, however, there was abundant precipitation exceeding 50 cm by mid-June at Temple and the criterion was

532 M. J . J E G E R A N D S . D. L Y D A

satisfied by JD 157, prior to extensive root rot development. In late August, PRR incidence in normal-density field plots of cotton was about 0.80.

DISCUSSION

The data examined in this study were obtained for different cultivars, planting dates, and sampling schemes, In this sense the data were incomplete and imperfect. As disease data were all obtained from the one site, it is to be expected that the forecasting criteria may not prove adequate for other sites. These inadequacies may be an advantage rather than a disadvantage, however, as any practical forecasting scheme devised would have to be robust enough to confront future variation in management practices even at one site. The problem is how, on the basis of imperfect knowledge or incomplete information, to devise a practical forecasting scheme, or indeed any other management strategy, that can operate at some acceptable level of failure when faced with future variations either within or between sites. With PRR there is an advantage in that symptom expression is unambiguous, is followed by plant death within a few days, and involves little assessment error. Provided that adequate plot sizes are used (Gerik et al., 1985), as was so in the data collection of this study, then reliable estimates of disease are made and provide little error in the construction or evaluation of a forecasting model.

There are several requirements for a practical forecasting scheme, given that it has been properly constructed and evaluated. The disease should be serious economically, and there should be variation in level of disease from season-to-season. There should be some, preferably economic, return for the grower in using the forecast. There should be time for the grower to respond to the forecast. In the case of Phymatotrichum root rot all but the last requirement was met, although the need to forecast may well be questioned when a major epidemic, on the basis of these data, can be expected in 50% of the years. The occurrence of PRR in many areas of the Blackland region is, localised however, and the high levels observed in the one field at Temple undoubtedly represent a worst rather than typical case. If a reliable prediction of a major root rot epidemic can be made at a sufficiently early stage in the growing season, then savings on other management costs can be made for fields with a known incidence of PRR in previous years. The grower can use his knowledge of individual fields known to be high risk to determine exactly where management costs can be saved. IPM programs now play a major role in cotton production (El-Zik & Frisbie, 1985) and the likelihood of high mortality due to PRR, which must confound the computation of costs and benefits associated with insecticide programs, cannot be ignored in areas such as the Blackland region where the pathogen is present. Arthropod pests such as the boll weevil, bollworm, tobacco budworm, and pink bollworm, attack the cotton plant at times when PRR symptoms are first observed, and insecticide sprays are applied, often based on economic thresholds, well into the summer months.

The last requirement for a forecasting scheme - that there must be time for the grower to respond to the forecast - was patently not met by the regression approach to analysing the historical data; it was necessary to include precipitation data up to the time of the last disease assessments in order to make a satisfactory forecast of final incidence. By this time, late in August, the losses due to PRR would be manifest. There are other problems with the regression approach. In many cases, as with the analyses described in this paper, a number of equations give a reasonable prediction of the dependent variable (Table 2). The problem is then one of choosing from these equations the one best or most suitable for the purpose at hand. A number of possibilities, some statistical and some pragmatic, have been proposed to facilitate this choice. Ideally, biological forethought should be used to provide a prior1 constraints and thus limit the number of analyses done in the first place. If a simple equation

Forecasting Phymatotrichum root rot incidence in cotton 533

developed in this way is statistically acceptable, but possibly less precise than a more complex equation, then it still may be more useful. In our case, we took the simplification even further and developed a qualitative threshold, rather than a quantitative equation, as a forecasting criterion.

We do not claim to have developed a forecasting scheme for PRR that could or should be adopted immediately without recourse to other information. It has not been possible to carry out sufficient evaluation of the criterion even in the Blackland region. However, we consider the criterion to be of some practical value provided its limitations are recognised. The one year, 1972, in which the criterion did not satisfactorily forecast a major epidemic, should be taken as an indication of where further epidemiological work is required in studies of PRR, rather than as an impediment to the use of a forecasting scheme. Any forecasting scheme must involve a compromise between ease of interpretation and use, and predictive power. For example, the discrepancy of 1972 can be lessened by manipulating the starting date for accumulating precipitation or by constructing a polynomial function of temperature to give more weight to this variable, which was not discrepant in 1972. A strong case can be made, however, for using cumulative precipitation from 1 January, which would be the information broadcast in weather bulletins for a particular location, and a simple measure of temperature as a basis for a practical forecasting scheme. In doing this, a certain failure rate, which means, on the basis of the historical data, that one year in 14 the criterion will not predict a major PRR epidemic, is accepted.

There are some restrictions that apply to the use of the forecasting criterion outside of the Blackland region, should such use be considered: (a) the soil type should be highly conducive, e.g. a vertisol or other alkaline clay soil; (b) there should be a known history of PRR and thus of the pathogen at the particular location; (c) the crop should be rain fed, so that precipitation is the major input to the soil-water balance; and (d) the criterion cannot be used to predict spatial expansion of areas infested.

Finally, there are alternative approaches to forecasting that we are concerned with, but without compromising the need for simplicity and ease of implementation. Such approaches are dependent upon climatological models being readily available to growers (Dugas & Heuer, 1985). These may include use of simple empirical evapotranspiration coefficients or more complex water balance models (Ritchie, 1972) for their utility in predicting the soil-root-fungal interaction. Few studies have reported the effects of soil-water balance on a strand-forming fungus such as P . omnivorum. With P . omnivorum there is the possibility of plant-to-plant spread by strand growth through the soil (Percy & Rush, 1985); strand architecture in the soil may be an important component of root rot epidemics (Alderman & Hine, 1982). Our field results to date indicate that major epidemics are consistent with plant-to-plant spread of the pathogen (Jeger et al., 1985) and that such spread essentially determines whether a major or minor epidemic will result.

We gratefully acknowledge the provision of climatological data by Dr W. A. Dugas, State Agricultural Climatologist, and the helpful comments of an anonymous reviewer and a statistical consultant.

REFERENCES ALDERMAN, S. C. & HINE, R. B. (1982). Vertical distribution in soil and induction of disease by strands of

ANON. (1984). 1983 Texas Field Crop Statistics. Texas Department of Agriculture and U.S. Department of Phymatotrichum omnivorum. Phytopathology 72, 409-4 12.

Agriculture Austin, Bulletin 221 July 1984, 96 pp.

534 M . J . J E G E R A N D S . D . L Y D A

BUTT, D. I . & ROYLE, D. I . (1984). Multiple regression in the epidemiology of plant diseases. In Epidemics Of’Plant Diseases: Mathematical Analysis and Modeling, pp. 78--114. Ed. J . Kranz. Springer-Verlag : Berlin.

DUGAS, W. A. & HEUER, M. L. (1985). A description of the Texas Agriciiltural weather advisory program software. The Texas Agricultural Experimenta Station. Miscellaneous Publication 1574 March 1985. 86 PP.

EL-ZIK, K. M. & FRISBIE, R . E. (1985). Integrated crop management systems for pest control and plant protection. Handbook of Naturally Occurring Pesticides: Vol. 1. Theory, Practice, and Methods of Detection. (Ed. N . Bushan Mandava), pp. 121-122. CRC Press, Inc., Boca Raton, Florida.

EZEKIEL, W. N. (1938). Cotton root rot in Texas in 1937, and conditions affecting its local prevalence. Plant Disease Reporter 22, 3 15-324.

GERIK, T. J . , RUSH, c. M. & JEGER, M. J. (1985). Optimizing plot size for field studies of Phymatotrichum root rot of cotton. Phytopathology 75, 240-243.

JEGER, M. J. , KENERLEY, c . M. & GERIK, T. J . (1985). Spatial dynamics of Phymatotrichum root rot in cotton. Phytopathology 75, 500 (Abstr.).

I.YDA, S. D. ( 1 978). Ecology of Phymatotrichum omnivorum. Annual Review of Phytopathology 16, 193- 209.

PERCY, R. G. (1 983). Potential range of Phymatotrichum omnivorum as determined by edaphic factors. Plant Disease 67, 981-983.

PERCY, R. G. & RUSH, c. M. (1985). Evaluation of four upland cotton genotypes for a rate-limiting resistance to Phymatotrichum root rot. Phytopathology 75, 463466.

RATLIFFE, G. T. (1934). Cotton root rot as affected by crop rotation and tillage at San Antonio, TX. Technical Bulletin No. 436, United States Department of Agriculture. 31 pp.

RITCHIE, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research 8, 1204- 12 13.

ROGERS, C. H. (1942). Cotton root rot studies with special reference to sclerotia, cover crops, rotations, tillage, seeding rates, soil fungicides, and effects on seed quality. Bulletin No. 614, Texas Agricultural E.xperiment Station, 45 pp.

STREETS, R. B. & BLOSS, H. E. (1973). Phymatotrichum root rot. Phytopathological Monograph No. 8 . American Phytopathological Society, St. Paul. 38 pp.

TAUBENHAUS, J . I. & DANA, B. F. (1928). The influence of moisture and temperature on cotton root rot. Buletin No. 386, Texas Agricultural Experiment Station, 23 pp.

(Received 16 October 1985)