Influence of cropping systems on foot and root diseases of winter wheat: fitting of a statistical model

European Journd

of

AgranomY ELSEVIER

European Journal of Agronomy 6 (1997) 61-77

Influence of cropping systems on foot and root diseases of winter wheat: fitting of a statistical model N. Colbach a,*, C. Duby b, A. Cavelier a, J.M. Meynard ’ b Departement

a INRA-SRIV, BP 29,3_%50 Le Rheu, France et Informatique, Institut National Agronomique Paris-Grignon, 16 rue Claude Bernard, 75231 Paris, Cedex 05, France ’ INRA-Agronomie, Centre de Grignon, 78850 Thiverval-Grignon, France

de Mathematique

Accepted

16 January

1996

Abstract In a series of trials carried out in France from 1986 to 1993, 503 winter wheat plots were assessed at heading for eyespot (Pseudocercosporella herpotrichoides), take-all (Gaeumannomyces graminis var. tritici) and sharp eyespot (Rhizoctonia cerealis). For each disease, a model was developed to estimate disease risk as a function of environment and cropping system. Eyespot depended on environment, crop succession, soil tillage (inversion vs. non-inversion), sowing date and tiller number per plant; take-all depended on environment, crop succession, sowing date and total nitrogen supply from soil plus fertiliser; and sharp eyespot depended on environment, crop succession, soil tillage and sowing date. Interactions between cultivation techniques were integrated by using a multiplicative model. The hierarchy of techniques is discussed. These models allow us to classify cropping systems according to their inherent disease risk and to foresee the impact of changes in system strategies. Comparison with previous studies also suggested new conclusions on the effect of “risk amplifying non-host crops”. 0 1997 Elsevier Science B.V. Keywords: Winter wheat; Pseudocercosporella Cropping system; Multiplicative model; Disease

herpotrichoides; risk

1. Introduction The major components of the foot and root disease complex of wheat are eyespot (Pseudocercosporella herpotrichoides (Fron) Deighton), sharp eyespot (Rhizoctonia cerealis Van der Hoeven) and take-all (Gaeumannomyces graminis (Sacc.) von Arx et Olivier var. tritici (Walker)). The latter infects the roots of wheat plants, the * Corresponding author. Station d’Agronomie, INRA, 17 rue Sully, BV 1540,21034 Dijon Cedex, France. 1161-0301/97/$17.00 0 1997 Elsevier Science PZZ S1161-0301(96)02033-3

B.V. All rights reserved

Gaeumannomyces

graminis;

Rhizoctonia

cerealis;

two former the stem bases. The eyespot fungus regularly develops resistance to commercial fungicides (Maraite and Weyns, 1986; Leroux and Gredt, 1988; Cavelier et al., 1988; Leroux and Marchegay, 1992) and no chemical control for the other two diseases has yet been developed: it is therefore important to optimize the management of cropping systems to limit the damage caused by these diseases. To help farmers to devise cropping strategies in the economic context of decreasing wheat and increasing input prices, there is a need for models

62

N. Colburh et ul. ! European Journal of Agronomy 6 (1997) 61-77

describing the influence of cropping systems on foot and root diseases of winter wheat. Several epidemiological models forecasting disease progress as a function of meteorological conditions have already been constructed for eyespot, either to synthesize existing epidemiological knowledge (Schriidter and Fehrmann, 1971a,b; Rapilly et al., 1979) or to decide whether or not to apply fungicides (Sieberasse and Fehrmann, 1987). The former models neglect any cropping system aspects and the latter may only be used to take tactical decisions for fungicide treatments. There have been many studies on the influence of one or two cropping techniques on diseases, but no synthesis at the cropping system level has yet been attempted. In this paper, we develop a series of statistical models quantifying the influence of cropping systems on disease risk while integrating interactions and hierarchy between factors. The aim of these models is to enable strategic decisions to be made on the best crops to precede wheat and how to grow wheat in order to minimise disease risk. The impact of diseases on yield is not considered in this paper.

B in Grignon); (b) soil tillage in interaction with crop succession ( 1993 on field B in Chartres and on field A in Grignon); (c) wheat crop management (1992 on field A in Chartres, in La Verribre and on field A in Le Rheu, 1993 on field B in Le Rheu); and (d) high- and low-input crop management in interaction with crop succession (1993 in Toulouse). Wheat cultivars were Top, Soissons or Fidel which are all highly susceptible to foot and root diseases. In total there were 503 plots. The field trials used complete block-designs with two (Grignon A), three (La Verriere) or four blocks, except in Toulouse where, in 1982, the design was completely randomised with a variable number of replications (three to eight) and, in 1993, there was only a single replicate. As the site by site analysis of the block designs showed however that blocks were usually without effect on diseases, we treated the blocks as replicates in this paper. 2.2. Output vuriuble: disease assessment The percentage of diseased plants was used as an output variable for each of the three models (Fig. 1). It was calculated after visual assessment of diseases. This was the only information available on all sites, even though not the most precise or relevant one to study disease impact on yield. However, the analysis of the various disease variables on those trials where both disease frequency and severity were available, showed that there were no important differences in results. To calculate the percentage of diseased plants, 30 (in the case of a four-block design) to 50 (in

2. Experimental methods and models 2.1. Location and design ofjeld trials To obtain the data used to generate the models, an extensive network of field trials was established (Tables 1 and 2) analysing (a) crop succession (1982 in Toulouse and from 1986 to 1993 on field Table 1 Description of the experimental sites Site

Chartres Grignon Le Rheu La Verriere Toulouse

Longitude

l”31’ l”58’ l”43’ 2”Ol’ l”67’

Latitude

48”28’ 48’51’ 48”Ol’ 48”46’ 43”39’

Altitude (m)

156 125 34 168 150

Soil texture Loam (%)

Silt (o/o)

Sand (%I

Mean annual temperature (“Cl

22-24 28 15 14-16 16-38

66-68 55 70 29-32 2843

5 17 15 38-42 28-55

10.3 10.2 10.2 10.1 12.5

Mean annual precipitation km) 581 635 714 639 764

N. Colbach et al. J European Journal of Agronomy 6 (1997) 61-77

63

Table 2 Crop succession and soil tillage established on each site Site

Chartres A 92

Successionx soil tillage”

Sowing date

Pre-previous crop

crop

Soil inversion

nh

h

Yes

Previous

no

h h nh nh

nh nh h h

h h nh nh nh nh

h h h h nh nh

no Yes no

Grignon B 86-93

h nh nh

h h nh

yes yes yes

La Verriltre 92

nh

h

yes

Chartres B 93

Grignon A 93

Le Rheu A 92

h

h

200 400

X

200 290

X

70 70 100

16 Ott

400

320

lcil

15 Dct

300

310

100

N 16 Ott

300

310

100

X

281 350

50

X

235 325

yes no yes

yes

h

yes

Toulouse 82

a h h h h h n n n

a a

yes

; h a n n

X

yes

h

a

f

8 Ott 1 Nov

Nitrogen fertiliser formd

Total nitrogen amountC

Yes no

Le Rheu B 93

Toulouse 93

X’

Sowing densityb (grains mu”)

X

X

X

11 Ott 6 Nov

X

17 Dct 26 Nov

X

16 Ott 24 Nov

X

80

250 125

250

125 250

X

X

50 50 100

228 325

50 50 100

3 Nov

350

250

50

30 Nov %20 Dee 15 Jan

280 325 400

190 250 280

50 50 50

no yes

; n n h n n

IlO

h n a n a h n

no no no

IlO yes

no no yes X

IlO no no

no

“h= host crop, a=risk amplifying non-host crop, n =other non-host crops. Example of take-all (see Table 3).bSowing density is given as a number of grains sown per m2. In our models, we used however the number of plants per m2 counted on each plot.“The total amount of nitrogen available for the crop is the sum of mineral nitrogen measured in soil at the end of winter, of mineral&d nitrogen resulting from crop residue and humus and of nitrogen applied as fertiliser.dThe form of the nitrogen fertiliser is given as a percentage of ammonium nitrogen.‘X = each level of the next column (here early and late sowing) exists for each level of the previous column (here only non-host/host succession followed by soil inversion).‘The level of the next column (here sowing on 16 Oct.) is the same for all levels of the previous column (here four crop succession x soil tillage combinations).

64

N. Colhach et al.

1Europeun Journal of’Agronomy 6 (1997) 61-77

the case of a two-block design) plants per experimental treatment were visually assessed for foot and root diseases, differentiating for each disease between infected and healthy plants. Data for the output variable was collected at heading, a stage considered sufficiently late to permit good disease expression (and therefore important differences between treatments) without reaching the maximum frequency (all plants infested) where all differences are “compressed”. However, for several experimental sites (Grignon B 86-93, Toulouse 82), diseases were only assessed at flowering. As this stage closely follows heading, disease increase may be presumed to be slight enough not to change its ranking among treatments. Differences in disease level due to this late assessment were integrated into the input variable environment (see later). 2.3.

input

vuriubles

2.3.1. Crop succession

Descriptions of crop succession take account of only the two immediately preceding crops. For convenience, the immediately preceding crop is described as “the previous crop”; the one before that as the “pre-previous crop”. Earlier crops might however be important for fungi that can survive as saprophytes for more than two years (for instance P. herpotrichoides: Mater, 1961a,b; Steinbrenner and Hiiflich, 1984) and for the development of disease decline in cereal monoculture. The preceding crops were classified (Table 3) as host crops (with a further differentiation between “strong” and “weak” hosts for eyespot), “risk amplifying non-host crops” and other non-host crops (Colbach et al., 1994). Whereas host crops tend to increase and non-host crops to decrease disease risk, “risk amplifying crops”, particularly maize and rye-grass, may increase risk only if they are associated with host crops. The mechanism responsible for disease risk increase by “amplifiers” is not known for eyespot and sharp eyespot; in case of take-all, it seems that these “amplifiers”

c

06l2l82430364248546066

Fig. 1. Distribution of output variables for 503 plots (percentage of diseased plants at heading). (a) Eyespot. (b) Take-all. (c) Sharp eyespot.

N. Colbach et al. / European Journal of Agronomy 6 (1997) 61-77

65

Table 3 Classification of crops according to their influence on foot and root diseases of winter wheat (Colbach et al., 1994) Take-all

Sharp eyespot

Eyespot

winter wheat winter barley spring barley

winter wheat winter barley spring barley

Strong:

Risk amplifying non-hosts

maize rye-grass

Non-hosts

sunflower luceme pea sorghum

maize rye-grass sorghum sunflower luceme pea

Host

have a depressive effect on soil microflora antagonistic to G. gruminis (Colbach et al., 1994). 2.3.2. Soil tillage All plots were tilled with a deep-working tool followed by superficial tillage. We only distinguished between “soil inversion” (plough) and “non-inversion” (chisel). The combination “preprevious crop x previous crop x soil tillage” determines the location of host residues in the soil profile. If it is correct to assume that ploughing always resulted in soil inversion, then the “preprevious crop x previous crop x soil tillage” combination describes the risk due to primary inoculum in the top layer, as there is a strong relationship between top layer host residues and primary infections (Cox and Cock, 1962; Colbach, 1994; Colbach and Meynard, 1995). The complete data set was unbalanced due to our analysing simultaneously several distinct field trials. As the data set was however connected for “environment” and “crop succession x soil tillage” factors (Table 2), estimation of effects was possible. Several effects might not be correctly evaluated by our models: for example, the impact of soil tillage after a “host/non-host” succession could only be evaluated at Chartres 93; as there was however little take-all on this location, no differences due to soil tillage could be observed. All other factors (sowing date, plant density, etc.) were treated as quantitative variables.

Weak:

winter wheat winter barley spring barley rye-grass

maize

sunflower lucerne pea sorghum

2.3.3. Sowing date Sowing date considerably influences eyespot (Steinbrenner and Seidel, 1982; Huet, 1986; Hornby et al., 1990; Groll and Luzi, 1991; Polley and Thomas, 1991), take-all (Steinbrenner and Seidel, 1982; Steinbrenner and Hiiflich, 1984; Hornby et al., 1990) and sharp eyespot (Cavelier and Hanrion, 1986; Polley and Thomas, 1991). Increased exposure of a crop to disease inoculum before winter is believed to be the main factor responsible for disease increase following early sowing. To evaluate the pre-winter infection risk, we used the same criteria as Chevalier-Gerard et al. (1994) for air-borne diseases and eyespot, the sum of degree-days (base 0°C) from sowing to 31 January (Rapilly et al., 1979). Actually, these authors worked only with P.h. herpotrichoides. We used the same criteria for P. h. acuformis, take-all and sharp eyespot, as no alternative models were available. In this paper, we will refer to this variable as “sowing date”. This variable integrates effects of weather, especially of temperature, on sporulation (only for eyespot), dissemination, infection and disease progress. “Sowing date” varied from 123 to 886 degree-days. 2.3.4. Plants rnp2 High plant density increases eyespot (Glynne, 1951; Salt, 1955; Glynne and Slope, 1959; Huet, 1986; Groll and Luzi, 1991) and, to a lesser degree, take-all (Glynne, 1951; Glynne and Slope, 1959)

66

N. Colbach et al. /European Journal of Agronomy 6 (1997) 61-77

and sharp eyespot (Cavelier and Hanrion, 1986). This effect may be explained by a smaller plantinoculum distance, and by reduced tillering (permitting a better sheath-tiller contact) in the case of foot diseases. For take-all, we used the variable “plants m - “’ counted in each plot. However, this variable was not available for Grignon B. The variable was introduced into the model for those locations where plant density was available; if it was not significant, the data from the Grignon B was also introduced into the model. “Plants mm2” varied from 30 to 400. 2.3.5. Tillers per plant For eyespot and sharp eyespot, we preferred the variable “number of fertile tillers per plant” (i.e., number of ears per plant at maturity) which integrates both mechanisms (plant-inoculum distance and sheath-tiller contact) and was available on all trial sites. “Tillers per plant” varied from 1 to 13. 2.3.6. Total available nitrogen Although large nitrogen doses are known to stimulate tillering, they increase eyespot (Meynard, 1985; Groll and Luzi, 1991) and sharp eyespot (Glynne, 1951; Cavelier and Hanrion, 1986). However, take-all is reported to be decreased by high nitrogen (Glynne, 1951; Glynne and Slope, 1959; Prew et al., 1986). We used the total quantity of available nitrogen, i.e., the sum of mineral nitrogen measured in the soil at the end of winter, of nitrogen mineralised from soil organic matter and crop residue and of applied nitrogen fertiliser (Rtmy and Hebert, 1977). “Total nitrogen” varied from 177 to 377 kg ha-‘. 2.3.7. Form of nitrogen fertiliser Applying ammonium fertiliser instead of ammonium nitrate considerably decreases take-all (Hornby and Goring, 1972; Smiley and Cook, 1973; Cook and Rovira, 1976; Lucas and Collet, 1988; Sarniguet et al., 1992a,b). This factor was coded to reflect the percentage of ammonium nitrogen in the applied fertiliser, i.e., ammonium sulphate equates to lOO%, ammonium nitrate to 50% and a combination of both (ammonium at

the first application, second) to 75%.

ammonium

nitrate

at the

2.3.8. Environment We call “environment” the combination of the geographical location and the harvest year of the assessed wheat crop. In Chartres, Grignon and Le Rheu, two different fields were used for the trials on each location. We therefore use the letters “A” and “B” to distinguish the different fields on a given location. In these models 16 different environments were used: Chartres A 92, Chartres B 93, Grignon A 93, La Verriere 92, Le Rheu A 92, Le Rheu B 93, Toulouse 82, Toulouse 93 and the 8 years (1986693) of the succession trial at Grignon, Grignon B 86 to Grignon B 93. The variable “environment” was considered as a “black box”. It indiscriminately integrated all those effects which could not be controlled and are not taken into account as variables. The various sites were only classified according to their impact on disease, without any detailed analysis of the mechanisms responsible. Part of the climatic risk was integrated into the variable “sowing date” by the use of a sum of degree-days. But all other pedoclimatic characteristics are a major component of the variable “environment”. This variable also integrated differences between trial sites due to cropping systems. Weed control, ear and leaf disease fungicides were, for instance, identical for all plots of a given trial, but varied from one location to another and their effects were not taken into account by an associated cropping system variable. The influence of these elements seemed however small compared to the impact of climate and soil and were therefore not discussed in the paper. “Environment” also took account of pathogen population differences between locations. In case of eyespot, this population was characterized by in vitro identifications: on each location and for each plot, 30 tillers with eyespot were chosen randomly, disinfected and incubated on Petri dishes with potato-dextrose-agar and antibiotics in order to identify the varieties of P. herpotrichoides. Plates were incubated for ca. 10 days at 20°C.

N. Colbach et al. / European Journal of Agronomy 6 (1997) 61-77

pathogens system.

2.4. Models 2.4.1.

61

and its interaction

with the cropping

Basic model

The choice of model form is based on two fundamental hypotheses: ( 1) every factor (environment, primary inoculum close to soil surface, sowing date, etc.) amplifies the disease risk due to the other cropping system elements; (2) if the value given to one of the input variables is nil, the output variable (disease incidence) is nil, i.e. if one factor puts disease at a strong disadvantage (cold and dry climate, sowing at 31 January etc.), then disease frequency is nil even if other factors tend to favour it. This last hypothesis lead us to choose a multiplicative model to explain disease incidence (a multiple regression model would not permit this result to be obtained). E(DI)=EN.SUxST.SD”.NPb.Nc.NFd

(1)

where: E(D1) = expected mean of DI; DI =per cent of diseased plants; EN = effect of environment (combination of location and year); SU x ST = effect of the interaction of crop succession (preprevious crop, previous crop) and soil tillage (inversion or non-inversion of soil layers). SD = sowing date (sum of degree-days from sowing to 31 January); NP = plants mm2 for take-all (replaced by TP = tillers per plant for eyespot and sharp eyespot); N = total nitrogen (kg ha-‘); NF = form of nitrogen fertiliser (percentage of ammonium nitrogen). “Environment” and “succession x soil tillage” “sowing date”, are qualitative variables; “plants mp2” and “tillers per plant”, “total nitrogen” and “fertiliser form” are quantitative variables of which the impact is modulated by the exponents a, b, c and d. The complex relationship between eyespot and sharp eyespot, due to a competition for the same infected wheat organ (Reinecke and Fehrmann, 1979) and possibly also to antagonism (Brtick and Schlosser, 1982), was not included into our models. This relationship is often expressed as a negative correlation between the two diseases (Reinecke and Fehrmann, 1979; Van der Hoeven and Bollen, 1980; Meunier, 1984; Cavelier et al., 1985; Colbach et al., 1994), but this description is insufficient to reflect correctly the relationship between the two

2.4.2. Linearized model There are two possible ways to analyse the model proposed above: ( 1) to treat it as a nonlinear model; (2) to linearize the model by logarithmic transformation. It was impossible to fit a non-linear model to eyespot data as the number of model parameters was too large to permit non-linear fitting ( 16 for EN, 14 for the SU x ST combination, four for SD, TP, N and NF). For take-all and sharp eyespot, both linear and non-linear models could be fitted to data. However, the analysis of the distribution of the residuals showed that non-linear fitting was not satisfactory and that the residuals were more likely to be distributed independently and normally, with a zero mean, for the linear than for the non-linear model. We may therefore suppose that the linearized model fitting was acceptable even for eyespot for which no non-linear model was possible. However, for many treatments, the logarithmic transformation applied to Eq. ( 1) was not possible as the output variable was zero. We therefore added a positive constant k to all output variables: E(ln(DI+k))=ln(EN)+In(SU +a.ln(SD)+b.ln(NP +d.ln(NF)

x ST) or TP)+c.ln(N) (2)

k had to be large enough to obtain a symmetric and random distribution for residual values, but small enough to keep differences between experimental treatments significant. Various models Eq. (2) with increasing k values were analysed, but only those with the best quality of fit and with a symmetric residual distribution are discussed. Model Eq. (1) was analysed with the NLIN procedure of SAS (Statistical Analysis System, SAS Institute Inc., 1989). Model Eq. (2) is a linear model which was analysed with the General Linear Model (GLM procedure) of the SAS software. The final models only contained those input variables for which the critical values of the test statistics were lower than 5%. If these values were

N. Colbach et al. / European Journal of Agronomy

68

higher than 5%, we considered that the contribution due to the input variable was not significant.

3. Results 3.1. Eyespot For Eq. (2), the transformation used (Table 4):

with k= 10 was

E( ln(DJeyespot+ 10)) = constant + ln( EN ) +ln(SU x ST)+a .ln(SD)+ b. ln(TP)

(3)

“Environment” was the most significant factor, followed by the “succession x soil tillage” combination, then by “sowing date”. “Tillers per plant” was the least significant variable; it explained less variability than the former three. EN estimations varied less at a given location (Grignon B, Toulouse) than between locations (Le Rheu 93 compared to La Verribre 92). For the combination SU x ST, we may distinguish several groups according to their resulting disease risk: (1) successions with a “strong host” as previous crop followed by a non-inversion of soil were responsible for the highest risk, (a pre-previous “amplifier” crop did not increase risk); (2) successions with a strong host as previous crop followed by a soil inversion, successions with a “weak host” as previous crop followed by a non-inversion and “strong host/amplifier” followed by soil inversion were responsible for a lower risk; (3) for all other successions, the multiplication of disease risk was estimated lower than 1. However, for “strong soil inversion host/non-host” successions, increased risk compared to non-inversion. The parameter associated with SD was positive: a longer “sowing date-3 1 January period” increased disease risk. The parameter associated with TP was negative: an increase of tillering reduced eyespot. Table 5 gives the proportions of P.h. herpotrichoides and acuformis identified on each site. 3.2. Take-all For the linear model, the transformation was used (Table 6):

k= 10

6 (1997) 61-77

E(MDL~e-.~l+ 1)) = constant + ln( EN) + ln(SU) +a.ln(SD)+b.ln(N) “Environment” was much more significant than the other variables. “Crop succession” was more significant than “sowing date”; “total nitrogen” was only barely significant and explained little variability. “Crop succession” and “sowing date” were considerably less significant than the “succession x soil tillage” combination and the variable “sowing date” respectively for eyespot. EN estimations varied less at a given location (Grignon B, Toulouse) than between locations (Grignon B 87 compared to Chartres A 92). For crop succession, several groups could be distinguished: (1) successions with a host as previous and an “amplifier” as pre-previous crops; (2) the other successions with a host as previous crop and the “host/amplifier” successions; (3) the “host/non-host” successions; (4) successions without host crops as previous and pre-previous crops. “Amplifier/host” successions were responsible for a considerably higher risk than the other successions with a host as previous crop. However, “host/amplifier” successions did not differ much from “host/host” successions. As for eyespot, the SD parameter was positive. The parameter associated with N was negative: an increase in “total nitrogen” resulted in a take-all decrease. The effect of the variable “plants mp2” was not significant on those locations where this information existed. 3.3. Sharp eyespot For Eq. (2), the transformation (Table 7):

k= 1 was used

E(ln(DLsharp eyespot+ 1)) = constant + ln( EN) +ln(SUxST)+a.ln(SD) “Environment” was again the most significant variable and explained most of the variability. However “sowing date” was the second most significant one, not “crop succession”. The “crop succession x soil tillage” combination and “sowing date” were considerably less significant than for

N. Colbach et al. / European Journal of Agronomy 6 (1997) 61-77

Table 4 Model explaining cropping system influence ln(constant)+In(SI)+hr(SUxST)+a~ln(SD)+b~ln(TP)

on

eyespot

at

heading:

the

final

model

69

was:

E(ln(DI,,,,,,+

Degrees of freedom

Sum of squares

Pr>F

r2

35 461 496

202.29 92.13 294.42

0.0001

0.69

15 18 1

74.28 48.40 17.04 2.64

lo))=

(A) Analysis of variance table Source

Model Error Corrected total EffeetS

ln(SI) ln(SU x ST) K

4.81 x 1O-5o 2.63 x 1O-32 3.08 17.04xx1O-4 lo-l9

(B) Estimations of the effects of the factors: Constant = -0.32 Esthnations for the factor site (Sl) Site Estimation of effect

Site

Estimation of effect

Grignon Grignon Grignon Grignon Grignon Grignon Grignon Grignon

Chartres A 92 Chartres B 93 Grignon A 93 La Verriere 92 Le Rheu A 92 Le Rheu B 93 Toulouse 82 Toulouse 93

-0.178 -0.120 -0.383 1.069 0.014 -0.746 -0.686 -0.325

B B B B B B B B

86 87 88 89 90 91 92 93

0.193 0.399 0.073 0.147 0.255 0.337 0.259 -0.314

JMimatiws for the combination crop succession x soil tillage Previous crop Strong host

Weak host

Soil inversion:

No

Yes

No

Pre-previous crop Strong host Weak host Risk amplifier Non-host

0.645 0.589 0.253 0.538

0.335 0.397

0.280

0.195

0.289

Risk amplifier Yes

No

-0.421

Non-host Yes

0.211 -0.396

-0.272

-0.665

No

Yes

-0.492

-0.145

-0.395 -0.378

-0.560

(C) Estimations of the parameters associated to the quantitative variables Quantitative variable

Parameter

Estimation

Sowing date Tillers per plant

;:

0.687 -0.318

eyespot, but “sowing date” was more significant than for take-all. EN estimations varied less at a given location (Grignon B, Toulouse) than between locations (Le

Rheu A 92 compared to Grignon B 87). For crop succession and soil tillage, several classes could be distinguished: ( 1) “host/amplifier” successions followed by soil inversion and “amplifier/host”

70

N. Colbach et al. / European Journal of Agronomy 6 ( 1997) 61-77

Table 5 Proportions of P.h. herpotrichoides and P. h. acujivmis identified on each site (proportions of tillers with eyespot revealing P. herpotrichoides varieties after superficial disinfection and incubation on Petri dishes with potato-dextrose-agar and antibiotics during 10 days at 20°C) Location Le Rheu

La Verriere

Chartres

Grignon

Toulouse 82

Year:

92

93

92

92

93

86693

P. h. acujormis P. h. herpotrichoides

70 31

69 60

0.2 100

3 100

2 99

0

0

0

100

100

100

93

Sums higher 100% were due to tillers revealing both P.h. varieties

successions followed by non-inversion, the former resulting in a higher risk than the latter; (2) “host/host”, “non-host/host” and “host/ amplifier” successions followed by a non-inversion. The “host/host” succession seemed to have been responsible for a slightly lower risk than the “nonhost/host” succession; (3) the other successions, of which the multiplication rate was estimated lower than 1. The SD parameter was positive.

4. Discussion 4.1, Influence of environment (EN)

Sites assessed at flowering (Grignon B 86-93, Toulouse 82) cannot be compared with those assessed at heading because the fungi had more time to infect and cause disease symptoms at the late-assessed locations. For eyespot, Rapilly et al. ( 1979) classified locations according to the probabilities that climatic conditions would favour eyespot sporulation and infection before winter and disease progress in spring. According to their criteria, the rankings for our locations should be approximately the following (most to least favourable environment): Le Rheu, La Verriere, Grignon and Chartres, Toulouse. Differences observed between this classification and the EN estimations could be partly explained by inter-annual climatic variability. However EN estimations showed that inter-annual variability on a given plot (differences between

Grignon B 86 to Grignon B 93 or between Toulouse 82 and 93) was low compared to variability between environments (for instance, between Le Rheu B 93 and La Verriere 92). The year effect is therefore not sufficient to explain the differences between our results and those of Rapilly et al. (1979). The estimations of Le Rheu A 92 and Le Rheu B 93 were those which were not coherent with Rapilly et al.: the former was estimated to be less favourable to disease than La Verriere 92 and the latter was estimated least favourable of all environments. Le Rheu A 92 and Le Rheu B 93 were the only two environments with a high proportion of P./z. acuformis (Table 5) whereas Rapilly et al. only worked with P.h. herpotrichoides. P.h. acuformis seems to be less aggressive (Fitt et al., 1987; Cavelier et al., 1988; Creighton et al., 1989) and to grow more slowly in planta (Mauler and Fehrmann, 1987a,b; Cavelier et al., 1987; Moreau et al., 1990). Furthermore, Le Rheu B 93 was the only site where both varieties were frequently found on a necrosis (Table 5). In vitro, antagonism was observed between the two varieties (Le Page, 1986) and in planta, a 50/50 variety mixture was less pathogenic than each variety inoculated separately (Cavelier et al., 1987). A mutual inhibition due to competition and/or antagonism might be the reason of the low Le Rheu B 93 estimation. As with eyespot (Ponchet, 1959; Schrbdter and Fehrmann, 1971a,b; Rapilly et al., 1979), take-all is known to be favoured by temperate and humid conditions (Garrett, 1934; Asher and Shipton, 1981): this explains why, except for Chartres, the

N. Colbach et al. / European Journal of Agronomy 6 (1997) 61-77

Table 6 Model explaining cropping ln(constant)+ln(SI)+ln(SU)+a~ln

system influence (SD)+b.ln(N)

on

take-all

at

heading:

the

final

11

model

is

E(ln(DI,,,,,,+l))=

Degrees of freedom

Sum of squares

Pr>F

r2

25 475 500

824.58 381.58 1206.43

0.0001

0.68

15 8

336.19 26.34

8.24 x lo-= 9.67 x 1O-5

1

2.95 7.00

5.59 x 10-3 3.31 1o-2

(A) Analysis of variance table Source Model Error Corrected total Effects ln(SI) ln(SU) ;:

(B) Estimations of the effects of the factors: Constant=4.335 Estimations for the factor site @I) Site Estimation of effect Grignon Grignon Grignon Grignon Grignon Grignon Grignon Grignon

B 86 B 87 B 88 B 89 B 90 B 91 B 92 B 93

0.841 1.252 1.131 0.925 1.130 1.099 0.937 0.758

Site

Estimation of effect

Chartres A 92 Chartres B 93 Grignon A 93 La Verriere 92 Le Rheu A 92 Le Rheu B 93 Toulouse 82 Toulouse 93

-2.528 -2.227 -0.704 - 1.671 0.040 -0.152 - 0.484 - 1.314

Estimations for the factor crop succession Pre-previous crop

Host Risk amplifier Non-host

Previous crop Host

Risk amplifier

Non-host

0.247 1.605 0.245

0.091 -0.380 -0.724

-0.108 -0.604 -0.369

(C) Estimations of the parameters associated to the quantitative variables Quantitative variable

Parameter

Estimation

Sowing date Total nitrogen

;:

0.420 -0.667

classification of environments for take-all is close to that given by Rapilly et al. The low EN estimators for Chartres A 92 and Chartres B 93 could be explained by low conduciveness to take-all in these environments (Colbach, 1995): take-all did not develop even if climatic conditions were favourable. For sharp eyespot, no previous work on “envi-

ronment” influence has been reported. Locations with important eyespot expression (mostly Grignon B 86-93) were usually not favourable to sharp eyespot, which is consistent with previous reports (Reinecke and Fehrmann, 1979; Briick and Schliisser, 1982; Meunier, 1984). For the other environments, classification for sharp eyespot strongly resembled the one for take-all: environ-

N. Colbach et al. /European Journal of Agronomy 6 (1997) 61-77

12

Table I Model explaining cropping system influence on sharp eyespot at heading: the final model is: E(ln(DI,r_.r eyespo,+1)) = ln(constant)+ln(SI)+ln(SU x ST)+a.ln(SD) Degrees of freedom

Sum of squares

Pr>F

29 472 501

400.82 434.78 835.60

0.000 1

15 13 1

240.00 59.68 21.62

9.06 x 10m4h 1.75 x 1om7 6.29 x lo-”

(A) Analysis of variance table Source Model Error Corrected total Effects ln(S1) ln(SU x ST) a

0.48

(B) Estimations of the effects of the factors: Constant = -4.013 Estimations for the factor site @I) Site Estimation of effect

Site

Estimation of effect

Grignon Grignon Grignon Grignon Grignon Grignon Grignon Grignon

Chartres A 92 Chartres B 93 Grignon A 93 La Verritre 92 Le Rheu A 92 Le Rheu B 93 Toulouse 82 Toulouse 93

-0.735 -0.670 0.700 0.713 1.840 1.057 -0.043 0.910

B 86 B 87 B 88 B 89 B 90 B 91 B 92 B 93

-0.528 -1.162 - 0.064 - 1.090 -0.195 -0.083 -0.013 -0.638

Estimations for the combination crop succession x soil tillage

Previous crop Host

Risk amplifier

Soil inversion

No

Yes

Pre-previous crop Host Risk amplifier Non-host

0.023 0.757 0.619

- 0.239 -0.049

No

0.275 - 0.625

Non-host Yes

No

Yes

1.125 - 1.258

-0.218 -0.173 - 0.075

-0.127 -0.037

(C) Estimations of the parameters associated to the quantitative variables Quantitative variable

Parameter

Estimation

Sowing date

u

0.839

ments with high take-all (Le Rheu A 92) were favourable to sharp eyespot; environments with low take-all conduciveness (Chartres A 92 and Chartres B 93) did not permit sharp eyespot expression either. The frequently observed negative correlation between eyespot and sharp eyespot

may however not only be explained by different climatic requirements; there seems also to be an interaction between the pathogens (due to competition and/or antagonism) which was however only important on sites with high eyespot infection (Meunier, 1984).

N. Colbach et al. /European

Journal of Agronomy 6 (1997) 61-77

4.2. Injuence of cropping system For eyespot and sharp eyespot, both crop succession and soil tillage were significant and the combinations of these factors may be ranked approximately as follows: successions with a host as previous crop followed by non-inversion; the same successions followed by soil inversion; “host/non-host” successions followed by inversion; the same successions followed by non-inversion; successions without hosts as previous or pre-previous crops. The classes used for “crop succession” and “soil tillage” are therefore consistent with the assumption that the disease risk increases with the amount of host crop residue, i.e. infectious crop residues, close to soil surface. Sharp eyespot risk was not estimated to be highest on “host/host” successions, which is not consistent with reports by Colbach et al. ( 1994). This underestimation could be due to the interaction between sharp eyespot and eyespot which was not integrated into our present sharp eyespot model. “Host/host” successions strongly favour eyespot, of which a high infection level limits sharp eyespot. Because of low take-all expression in Chartres B 93 which was one of the few sites with soil inversion and non-inversion, the soil tillage effect could not be correctly estimated for take-all. SU estimations are however consistent with those of Colbach et al. (1994) and crop successions can approximately be ranked as follows: “host/host”, “non-host/host”, “host/non-host” and “nonhost/non-host” successions. This classification is consistent with that given by Steinbrenner and Obenauf (1986). The classes used for crop succession correctly reflect the risk due to the total amount of infectious crop residues on the plots (without differentiating the various soil layers). Besides verifying the consistency of our results with previous reports (see above), the analysis of the SU x ST estimations suggested several new conclusions: l the comparison of “host/amplifier” and “host/non-host” successions followed by soil inversion and non-inversion shows that residues left by a pre-previous host and carried back to the soil surface by soil inversion, were only

l

l

73

responsible for a high disease risk if the previous crop was an “amplifier”. for take-all and sharp eyespot, “amplifier/host” successions were responsible for a higher risk than other successions with the same previous crop as for instance “host/host” successions. For these two diseases, the risk amplifying effect was thus also important for pre-previous “amplifier” crops, whereas Colbach et al. (1994) worked only with previous “amplifier” crops. In both positions, the risk amplification was more important for take-all and sharp eyespot than for eyespot. However, the “amplifier/host” succession was only present twice (Table 2) in a low-risk environment where no “host/host” successions were present (Toulouse 93). Risk estimation for “amplifier/host” successions was therefore probably overestimated compared to “host/host” successions. previous hosts resulted in a higher risk than pre-previous hosts probably because residue infectivity decreases with time as shown for eyespot and take-all by Steinbrenner and Hiiflich ( 1984). This decrease could however be faster for sharp eyespot: for this disease, soil inversion vs. non-inversion following “host/non-host” successions resulted in a lower disease increase than for eyespot.

The sowing date effect estimated by the three models, i.e., the disease risk increase in case of an increasing sum of degree-days from sowing to 31 January, is consistent with previous reports (see above). The increase in eyespot in the case of low tiller number per plant due to high plant density is also consistent with previous reports (see above). However, take-all and sharp eyespot were not influenced either by tillering or plant density. The few previous reports on such effects may also reflect their unimportance. The impact on sharp eyespot might be confused by interactions with eyespot which were not integrated into our model. Take-all might be less dependent on plant density at long term, as at the assessed stage, root development should be important enough to compensate high plant-inoculum distances in case of low plant density.

14

N. Colhuch et ul. / European Journul of Agronomy 6 ( 1997) 61-77

The take-all increase in the case of a decrease in total nitrogen is consistent with previous reports. Nitrogen effect on foot diseases did not seem strong enough to be significant in the final models. The previously reported take-all decrease with the application of fertilizer nitrogen as ammonium did not appear in our models. It is possible that this effect is generally not important enough to be significant in a synthetic model. The absence of an effect of nitrogen fertiliser form might however also be due to the unbalanced data set used for model building, at least in case of take-all. The various fertiliser forms were only tested at a small number of sites where previous crops were always host crops. Sarniguet et al. (1992a) observed that in case of high take-all risk, ammonium nitrogen did not stimulate antagonistic microflora sufficiently to limit disease frequency, even if disease severity could be decreased. It is therefore possible that ammonium nitrogen reduces take-all after unfavourable crop successions. The model form used for our work would therefore not be appropriate to describe “crop succession x fertiliser form” interactions. 4.3. Hierarchy of effects The hierarchy of effects was similar for take-all and eyespot: the “environment” effect was always dominant because it influenced both disease development on previous crops (and therefore the amount of infectious crop residues) and on the assessed wheat crop. The other factors influencing the amount of infectious crop residues (SU x ST for eyespot, SU for take-all) were also of considerable importance. This effect was however estimated less important for take-all: SU reflected the total amount of host crop residues on the plots, and not only the amount of host residues close to soil surface illustrated by the combination SU x ST for eyespot. Risk estimation was therefore more precise for the latter. The “sowing date” effect was more important for eyespot, probably because of its strict climatic requirements for sporulation, dissemination and infection. The take-all fungus develops in the soil; it might therefore depend less on climatic conditions and the expression of pre-winter infection risk as a sum of degree-days might be less appropriate for take-all than for eyespot.

The other variables determining the probability of inoculum-plant contact for eyespot (TP) and the stimulation of antagonistic microflora by nitrogen for take-all (N), were of small importance. The hierarchy would have been similar for sharp eyespot if the SU x ST impact had not been so small. However, this divergence (of which the under-estimation of sharp eyespot after “host/host” successions could be an aspect) could be due to interactions with eyespot which were not integrated into our model. “Sowing date” impact for sharp eyespot seems to have been situated halfway between those for eyespot and take-all which could be due to a certain resemblance between sharp eyespot and the latter diseases. Both sharp eyespot and take-all infect plants by mycelial growth (and therefore without any strict climatic requirements for the production of dissemination units), whereas both P. herpotrichoides and R. cerealis infect the sheaths and tiller bases of their host plants. Temperature variations integrated into the SD variable could therefore influence sharp eyespot more strongly than take-all. The non-significance of various techniques for which previous reports observed impacts on diseases, could have been due to: (a) our disease assessment stage which could have been too late to observe effects. This could be true for plant density and take-all; (b) the unbalanced data set and the model form which did not permit a correct estimation of the effects and interactions of these techniques. This could have been true for “nitrogen fertiliser form” and “soil tillage” effects on take-all. 4.4. Modelfbrm Model quality was satisfactory for eyespot and take-all. Residual values never depended on input variables or on predicted values. The disease variations due to input variations were always consistent with previous reports. The chosen model form is therefore appropriate for disease risk estimation despite of our unbalanced data set.

5. Conclusion In our models, the environment effect was treated as a “black box”. Of course, an under-

N. Colbach et al. / European Journal of Agronomy 6 (1997) 61-77

standing of the basis of the important environment effect would considerably help to develop integrated cropping systems. However, the present models permit us to infer the consequences of cropping system changes on disease risk, as follows: If, in a wheat/non-wheat succession favourable to disease development, the non-wheat is a pea or a sunflower instead of a maize crop, sharp eyespot is reduced by 70%, eyespot by 30% and take-all by 20%. For a given crop succession (for instance cereal/non-cereal) and without changing the yield objective (and therefore nitrogen fertilization), current practice such as mid-October sowing following soil inversion are responsible for considerable disease increases (+ 90% for eyespot, + 40% for sharp eyespot, +20% for take-all) compared to earlyNovember sowing following a non-inversion. Similarly, the models show that the intensification of the early eighties (earlier sowing by 3 weeks, increase of sowing density by 30%, increase of total nitrogen by 15%; Meynard, 1991) resulted in a considerable eyespot increase (+33%) whereas disease increase was less important for sharp eyespot (+ 18%) and take-all (+ 3%). These models should allow us to develop cropping system strategies which decrease disease risks, especially for eyespot. For take-all and sharp eyespot, our models must first be improved before the development of such strategies will be possible. Efficient disease control based on cropping systems should be possible for eyespot (thus decreasing fungicide use) but less likely for sharp eyespot and take-all.

Acknowledgment

We thank M. Cotten (INRA Rennes), P. Debaeke and J.R. Marty (INRA Toulouse), B. Mille (INRA La Verriere), J. Troizier (INRA Grignon), H. Yvrard (Lycee agricole de Chartres) for conducting our field trials and N. Cavelier, P. Huet and P. Lucas for allowing us the use of their disease assessment data.

75

References Asher, M.J.C and Shipton, P.J., 1981. Biology and control of take-all. In: M.J.C. Asher and, P.J. Shipton (Editors), Biology and Control of Take-all. Academic Press, London, pp. 317-341. Brtick, K.P. and Schlosser, E., 1982. GetreidefuDkrankheitserreger. V. Antagonismus zwischen den Erregem. Z. Pflanzenkr. Pflanzenschutz, 89: 337-343. Cavelier, N. and Hanrion, M., 1986. Etude des facteurs favorables au developpement de rhizoctone des &&ales. Perspect. Agric., 105: 30-34. Cavelier, N., Lucas, P. and Boulch, G., 1985. Evolution du complexe parasitaire constitue par Rhizoctonia cerealis et Pseudocercosporella herpotrichoides, champignons parasites de la base des tiges des &eles. Agronomie, 5: 693-700. Cavelier, N., Rousseau, M. and Le Page, D., 1987. Variabilite de Pseudocercosporella herpotrichoides, agent du pi&in-verse des cereales; comportement in vivo de deux types d’isolats et dune population en melange. Z. Ptlanzenkr. Pllanzenschutz, 94: 590-599. Cavelier, N., Prunier, M. and Creighton, N.F., 1988. Variabilitt intrasp&t!que de Pseudocercosporella herpotrichoides, agent du pi&in-verse. des c&ales. Pouvoir pathogene de 2 types de souches. 2eme Conference Intemationale sur les maladies des plantes (ANPP Bordeaux), Tome 1, pp. 227-235. Chevalier-Gerard, C., Denis, J.B. and Meynard, J.M., 1994. Perte de rendement due aux maladies cryptogamiques sur ble tendre d’hiver. Construction et validation d’un modele de l’effet du sysmme de culture. Agronomie, 14: 305-3 18. Colbach, N., 1994. Influence of crop succession and soil tillage on wheat take-all (Gaeumannomyces graminis var. tritici). Proc. 3rd ESA Congress, Abano-Padova 1994, 672-673. Colbach, N., 1995. Moddlisation de l’influence des systknes de culture sur les maladies du pied et des racines du b16 tendre d’hiver. These de doctorat de 1’Institut National Agronomique Paris-Grignon, 237 pp. Colbach, N. and Meynard, J.M., 1995. Soil tillage and eyespot: influence of crop residue distribution on disease development and infection cycles. Eur. J. Plant Pathol. 101: 60-611. Colbach, N., Lucas, P. and Cavelier, N., 1994. Influence des successions culturales sur les maladies du pied et des racines du ble d’hiver. Agronomie 14: 525-540. Cook, R.J. and Rovira, A.D., 1976. The role of bacteria in the biological control of Gaeumannomyces graminis by suppressive soils. Soil Biol. Biochem., 8: 269-273. Cox, J. and Cock, L.J., 1962. Survival of Cercosporella herpotrichoides on naturally infected straws of wheat and barley. Plant Pathol., 11: 65-66. Creighton, N.F., Cavelier, N. and Fitt, B.D.L., 1989. Pathogenicity to wheat and rye of Pseudocercosporella herpotrichoides isolates from France and the UK. Mycol. Res., 92: 13-17. Fitt, B.D.L., Creighton, N.F. and Bateman, G.L., 1987. Pathogenicity to seedlings of wheat-type and rye-type isolates of Pseudocercosporella herpotrichoides. Trans. Br. Mycol. sot., 88: 149-155.

76

N. Colbach et al. J European Journal of Agronomy 6 (1997) 61-77

Garrett, SD., 1934. Factors affecting the severity of take-all, III. The climatic factor. J. Dep. Agric. South Aust.. 37: 976-983. Glynne, M.D., 1951. Effect of cultural treatments on wheat and on the incidence of eyespot lodging, take-all and weeds. Ann. Appl. Biol., 38: 6655688. Glynne, M.D. and Slope, D.B., 1959. Effects of previous wheat crops, seed rate and nitrogen on eyespot, take-all, weeds and yields of two varieties of winter wheat. Ann. Appl. Biol.. 47: 187-199. Groll, U. and Luzi, K., 1991. Untersuchungen zum Einflul3 und pflanzenbaulicher Faktoren auf den ackerHalmbruchbefall an Wintergetreide. Arch. Phytopathol. PlIanzensch., 27: 459-470. Hornby, D. and Goring, C.A.I., 1972. Effects of ammonium and nitrate nutrition on take-all disease on wheat in pots. Ann. Appl. Biol., 70: 2255231. Homby, D., Bateman, G.L., Gutteridge, R.J., Lucas, P., Montfort, F. and Cavelier, A., 1990. Experiments in England and France on fertilisers, fungicides and agronomic practices to decrease take-all. Brighton Crop Protection Conference Pests and Diseases - 1990, pp. 771-776. Huet, P., 1986. Influence du systeme de culture sur le pietinverse du blk. In: Les rotations cerealieres intensives. Dix an&es d’ttudes concert&es INRA-ONIC-ITCF, 1973-1983. INRA, Paris, pp. 95-l 11. J_e Page, D., 1986. Epidemiologic des souches de Pseudocercosporella herpotrichoides (Fron) Deighton, agent du pi&in-verse des c&ales, resistantes aux fongicides benzimidazoles et thiophanates. These 3eme Cycle, Universite Rennes I, 91 pp. Leroux, P. and Gredt, M. 1988. Caracterisation des souches de Pseudocerosporella herpotrichoides, agent du pi&in-verse des c&&ales, rtsistantes a des substances antimitotiques et a des inhibiteurs de la biosynthtse des sttrls. Agronomie, 8: 719-729. Leroux, P. and Marchegay, P. 1992. Variabilite chez l’agent du pi&in-verse des c&ales: implications pratiques. Phytoma, 437: 25-30. Lucas, P. and Collet, J.M. 1988. Influence de la fertilisation azotee sur la receptivite d’un sol au pittin-echaudage, le dtveloppement de la maladie au champ et les populations de Pseudomonas fluorescents. Bull. OEPP, 18: 1033111. Mater, R.C.F. 1961a. Saprophytic colonization of wheat straw by Cercosporella herpotrichoides Fron and other fungi. Ann. Appl. Biol., 49: 152-164. Mater, R.C.F. 1961b. Survival of Cercosporella herpotrichoides Fron in wheat straw. Ann Appl. Biol., 49: 165-172. Maraite, H. and Weyns, J. 1986. Evolution of MBC-resistance in Pseudocerosporella herpotrichoides on winter wheat and winter barley. Meded. Fat. Landbouwet. Rijksuniv. Gent, 51: 691-697. Mauler, A. and Fehrmann, H. 1987a. Erfassung der Anfalligkeit von Weizen gegenilber Pseudocerosporella herpotrichoides. I. Untersuchtmgen znr Pathogenitat verschiedener Formen des Erregers. Z. Pllanzenkr. Pflanzenschutz, 94: 630-636. Mauler, A. and Fehrmann, H. 1987b. Erfassung der

Anfalligkeit von Weizen gegentiber Pseudocerosporella herpotrichoides. II. Zur Entwicklung eines Resistenztests an Jungpflanzen. Z. Pflanzenkr. Pflanzenschutz, 94: 637-648. Meunier, S. 1984. Inventaire des maladies du pied chez le froment d’hiver en 1983 et incidence sur les composantes du rendement. Parasitica, 40: 27740. Meynard, J.M. 1985. Construciton d’itineraires techniques pour la conduite du blt d’hiver. These de docteur-ingenieur de 1’Institut National Agronomique Paris-Grignon, 297~. Meynard, J.M. 1991. Pesticides et itineraires techniques. In: P. Bye, C. Descoins and A. Deshaye (Editors), Phytosanitaires, Protection des plantes, Biopesticides. Paris: SCrie “Les Colloques de l’INRA”, pp. 85-100. Moreau, J.M., Van Schingen, J.C. and Maraite, H., 1990. Epidemiologic de Pseudocercosporella herpotrichoides var. acuformis et var. herpotrichoides sur froment d’hiver, demonstration dun cycle secondaire. Meded. Fat. Landbouwwet. Rijksuniv. Gent, 55: 889-898. Polley, R.W. and Thomas, M.R., 1991. Surveys of diseases of winter wheat in England and Wales, 1976-1988. Ann. Appl. Biol., 119: l-20. Ponchet, J., 1959. La maladie du pi&in-verse des c&ales, importance agronomique, biologie et tpiphytologie. Ann. Epiphyt., 1: 45-98. Prew, R.D., Beane, J., Carter, N., Church, B.M., Dewar, A.M., Lacey, J., Penny, A., Plumb, R.T., Thorme, G.N. and Todd, A.D., 1986. Some factors affecting the growth and yield of winter wheat grown as a third cereal with much or negligible take-all. J. Agric. Sci. Cambridge, 107: 6399671. Rapilly, F., Laborie, Y., Eschenbrenner, P., Choisnel, E. and Lacroze, F., 1979. La prevision du pi&in-verse sur ble d’hiver. Perspect. Agric., 49: 30-40. Reinecke, H. and Fehrmann, H., 1979. Rhizoctonia cerealis Van der Hoeven an Getreide in der BRD. Z. Pflanzenkr. Ptlanzenschutz, 86: 190@204. R&my, J.C. and Hebert, J., 1977. Le devenir des engrais azotes dans le sol. C.R. Acad. Agric. Fr., 63: 700-710. Salt, G.A., 1955. Effects of nitrogen applied at different dates, and of other cultural treatments on eyespot, lodging and yield of winter wheat. Field experiment 1952. J. Agric. Sci., 46: 407--416. Sarniguet, A., Lucas, P. and Lucas, M., 1992a. Relationships between take-all, soil conduciveness to the disease, populations of fluorescent pseudomonads and nitrogen fertilizer. Plant Soil, 145: 17-27. Sarniguet, A., Lucas, P. and Lucas, M., 1992b. Soil conduciveness to take-all of wheat. Influence of the nitrogen fertilizers on the structure of populations of fluorescents pseudomonads. Plant Soil, 145: 29936. SAS Institute Inc., 1989. SASjSTAT User’s Guide, Version 6. Cary NC USA: SAS Institute Inc., Fourth Edition. Schrodter, H. and Fehrmann, H., 1971a. iikologische Untersuchungen zur Epidemiologie von Cercosporella herpotrichoides. II. Die Abhiingigkeit des Infektionserfolges von einzelnen meteorologischen Faktoren. Phytopathol. Z., 71: 977112. Schrodter, H. and Fehrmann, H., 1971b. Gkologische

N. Colbach et al. J European Journal of Agronomy 6 (1997) 61-77

Untersuchungen zur Epidemiologie von Cercosporelia herpotrichoides. III. Die relative Bedeutung der meteorologischen Parameter und die komplexe Wirkung ihrer Konstellationen auf den Infektionserfolg. Phytopathol. Z., 71: 203-222. Sieberasse, G. and Fehrmann, H., 1987. Ein erweitertes Model1 zur praxisgerechten Bekarnpfung des Erregers der Halmbruchkrankheit Pseudocercosporella herpotrichoides in Winterweizen. Z Pflanzenkr Pllanzenschutz 2: 137-149. Srniley, R.W. and Cook, R.J., 1973. Relationship between takeall of wheat and rhizosphere pH in soils fertilized with ammonium vs. nitrate nitrogen, Phytopathology, 63: 882-889. Steinbrenner, K. and Hbllich, G., 1984. EinfluB acker- und pflanzenbaulicher MaBnahmen auf den Befall des Getreides durch Pseudocercosporella herpotrichoides (Fron) Deighton

II

und Gaeumannomyces graminis (Sacc.) Arx et Olivier. Arch. Phytopathol. Pflanzensch, 20: 469-486. Steinbrenner, K. and Obenauf, U., 1986. Untersuchungen zum EinfluB der Vorfrucht und Vorvorfrucht auf den Ertrag von Wintergetreidearten und den Befall durch Gaeumannomyces graminis. Arch. Acker-Ptlanzenbau Bodenkd., 30: 773-779. Steinbrenner, K. and Seidel, D., 1982. Komplexe Benutzung acker- und pflanzenbaulicher MaBnahmen im Rahmen des Pllanzenschutzes. Nachrichtenbl. Pllanzenschutz, 7: 137-140. Van der Hoeven, E.P. and Bollen, G.J., 1980. Effect of benomyl on soil fungi associated with rye. Effect on the incidence of sharp eyespot caused by Rhizoctonia cerealis. Neth. J. Plant Pathol.. 86: 163-180.