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Australasian Plant PathologyJournal of the Australasian PlantPathology Society ISSN 0815-3191Volume 40Number 6 Australasian Plant Pathol. (2011)40:591-600DOI 10.1007/s13313-011-0034-1

A meta-analysis of severity and yieldloss from ascochyta blight on field pea inWestern Australia

Moin U. Salam, William J. MacLeod,Tim Maling, Ian Prichard, MarkSeymour & Martin J. Barbetti

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A meta-analysis of severity and yield loss from ascochytablight on field pea in Western Australia

Moin U. Salam & William J. MacLeod & Tim Maling &

Ian Prichard & Mark Seymour & Martin J. Barbetti

Received: 2 November 2010 /Accepted: 26 January 2011 /Published online: 23 March 2011# Australasian Plant Pathology Society Inc. 2011

Abstract A meta-analysis of severity and yield loss fromascochyta blight (AB) on field pea was performed using 18field experiments conducted over eight seasons in 13locations in Western Australia (WA). The severity of AB,across the WA grain-belt, reached its maximum limit (ABseverity 5) during mid-April sowing and linearly declinedto almost nil by mid-July sowing. Pre-sowing rainfall had asignificant effect on AB severity: the more rainfall eventsthat occurred the less was the disease severity. The regionaland seasonal difference in the rate of decline of the ABseverity (slope of the regression line) with delay in sowingwas not significant, whereas the intercepts were largelysignificant indicating there were differences in the initialAB severity status between the regions and seasons.Fungicide control [Impact® in-furrow (flutriafol) applica-tion or fortnightly sprays of Bravo® (chlorothalonil)] didnot reduce the disease severity in early sowing (weekending 7 May). The yield loss due to AB was calculated as

10.3% per AB severity unit, indicating that a loss of ≥50%could occur with the highest severity. Magnet showedsignificantly higher yield loss compared to all other varieties.The differences in yield loss between the five regions were notsignificant. On the other hand, a dry finishing season resultedin significantly higher yield loss than a wet finishing season.These analyses will help design improved strategies for ABmanagement in field pea in Western Australia.

Keywords Ascochyta blight . Ascochyta pisi . Ascosporeshower . Blackspot . Disease severity .Didymella pinodes .

Field pea .Mycosphaerella pinodes . Phoma koolunga .

Phoma medicaginis var. pinodella . Quantitativeepidemiology . Yield loss

Introduction

The known causal agents of ascochyta blight (AB;synonym: blackspot) on field pea (Pisum sativum L.)include Didymella pinodes (synonym: Mycosphaerellapinodes), Phoma medicaginis var. pinodella, Ascochytapisi and Phoma koolunga (Bretag et al. 1995; Davidson etal. 2009; Peever et al. 2007). Although these pathogensexist independently, some or all may occur together as acomplex. D. pinodes is predominant in field pea crops inAustralia (Davidson et al. 2009). Management of ascochytablight is an essential practice of growing field pea inAustralia (Salam and Galloway 2005) as the disease isdevastating, causing as much as 75% yield loss (McDonaldand Peck 2009). As commercial varieties generally haveinadequate disease resistance (Bretag et al. 2006), and asfoliar sprays are uneconomical (Bretag 1985), AB manage-ment predominantly relies on cultural options. Infested fieldpea stubble is the main source of primary inoculum in

M. U. Salam (*) :W. J. MacLeod : T. Maling : I. PrichardDepartment of Agriculture and Food Western Australia,Locked Bag 4, Bentley Delivery Centre,Bentley, WA 6983, Australiae-mail: moin.salam@agric.wa.gov.au

M. SeymourDepartment of Agriculture and Food Western Australia,PMB 50, Melijinup Road,Esperance, WA 6450, Australia

M. J. BarbettiSchool of Plant Biology, The University of Western Australia,35 Stirling Highway,Crawley, WA 6009, Australia

M. J. BarbettiThe Institute of Agriculture, The University of Western Australia,35 Stirling Highway,Crawley, WA 6009, Australia

Australasian Plant Pathol. (2011) 40:591–600DOI 10.1007/s13313-011-0034-1

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Australia (Bretag et al. 2006). The AB epidemics arepredominantly initiated by airborne ascospores releasedfrom mature pseudothecia (ascocarps) which develop on theinfected crop stubble, largely from the previous year(Bretag 1991; Salam et al. 2003a). Given this scenario,suggested AB management options include destruction ofstubble, widening crop rotation and maintaining a distanceof 500 m from previous year’s crop (Davidson and Kimber2007). However, under on-farm situations, especially whenthe crop is grown in a large scale, it is most unlikely thatsufficient stubble can be destroyed to eliminate the sourceof primary inoculum. Further, ascospores can disperse todistances of over 1.6 km (Schoeny et al. 2007), which iswell over the suggested distance (500 m) of cropseparation. Considering these factors, the principal strategysuggested for management of AB in field pea is to delaysowing as long as possible towards the end of the sowingwindow to avoid the majority of ascospores falling on theemerging field pea seedlings (Salam et al. 2011).

Studies in New South Wales, South Australia andVictoria, show that early sown field pea crops are likelyto become more severely infected than later sown crops(Heenan 1994; Bretag et al. 2000; Davidson and Ramsey2000). While similar findings have been suggested forWestern Australia, they are not well documented. It hasbeen argued that the amount of air-borne spores available toinfect crops is usually higher early in the season (Bretag1991) and that the disease has longer to develop on earlysown than late sown crops (Bretag et al. 2000). Theavailability of air-borne spores in fact depends on pre-seasonal conditions leading to the time of sowing, andvaries between the years within a region and between theregions within a year (Salam et al. 2011), somethingaccounted for in few studies to date.

Selecting a time of sowing or any other options tomanage AB on field pea ultimately aims to reduce theyield loss. Bretag et al. (2006) mentioned that the mostreliable estimates of yield losses come from fungicidecontrol experiments where the yields of peas with andwithout disease are compared. Using this methodology,Bretag et al. (1995) found that disease severity was closelycorrelated with reductions in grain yield, and for mostvarieties there was a 5–6% reduction in grain yield forevery 10% of stem area affected by disease (first 10internodes on the main branch). However, in field experi-ments observations of disease do not always encompassthe whole scale of severity, and as such yield lossestimations are often extrapolations from an incompleterange of severities (e.g. Bretag et al. 1995). In addition,the yield of field pea can often be influenced by time ofsowing (McMurray et al. 2011) and the disease relatedyield losses can be confounded by yield variation relatingto time of sowing.

These issues discussed above cannot be dealt with byanalysing a few experimental results, as has been theapproach in the past. Meta-analysis provides the bestopportunity to address these issues as it provides aquantitative approach that estimates a relative responsefrom individual studies to identify general trends anddifferences. To best of our knowledge, a similar analysishas not been performed in relation to AB on field pea inAustralia.

In this paper, using 18 field experiments conducted over8 seasons in 13 locations, we quantify the effect of time ofsowing on the severity of AB on field pea across WesternAustralia and its regions at different seasons. We quantifyyield loss in response to disease severity, and analyse anddefine the extent to which the severity of the disease can becontrolled by using fungicides.

Materials and methods

Data source and description

Data, mostly unpublished, from 18 experiments thatmeasured AB severity on field pea were retrieved fromthe data archive system of the Department of Agricultureand Food Western Australia (DAFWA). In addition, rawdata were accessed from the personnel (I. Prichard, W.J.MacLeod and M. Seymour) who, along with M.J. Barbetti,were principally responsible for conducting these experi-ments. Five of those experiments were conducted in 1989,two in 1996, three in 1997 and 1998, two in 1999, one eachin 2002, 2003 and 2005 (Table 1). South western WA wasdivided into 5 agricultural regions (Fig. 1). Two experi-ments were conducted in the North, four in the CentralWheatbelt West (CWB-W), five in the Central WheatbeltEast (CWB-E), three in the South West and four in theSouth East region. Dundale (dun seed, conventional leaf)was the predominant variety used in the experiments;others included, alphabetically, Alma (dun seed, conven-tional leaf), Dunwa (dun seed, conventional leaf), Helena(dun seed, conventional leaf), Kaspa (dun seed, semi-leafless), King (dun seed, conventional leaf), Laura (whiteseed, conventional leaf), Magnet (dun seed, semi-leafless),Parafield (dun seed, conventional leaf), Pennant (whiteseed, conventional leaf), Snowpeak (white seed, semi-leafless), breeding line WAP2013 (dun seed, conventionalleaf), breeding line WAP2022 (dun seed, semi-leafless), andWirrega (white seed, conventional leaf). Time of sowingwas the common treatment included in all experiments,with between 2 and 4 times of sowing (Table 1). Othertreatments that interest this study included in-furrowfungicide application of Impact® (flutriafol) at 400 or600 g ai ha−1, and fortnightly fungicide sprays of Bravo®

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(chlorothalonil) at 1.8 kg ai ha−1 from about 2 weeks afteremergence until the end of flowering. Most of theexperiments consisted of four replications, while othershad three replications. Plot size varied from 1.5m×20m to2m×20m. All the experiments were conducted undernaturally occurring disease conditions. However, to ensurepresence of AB disease, the experimental fields wereselected on top of or close to previous year’s infected fieldpea stubble. Sowing densities aimed for 25, 50 or 100plants m−2. Plots were machine-harvested at maturity forgrain yield and the plot yield converted to t ha−1. Theseverity of AB disease was measured on stems. Stemdisease was scored at the end of the flowering window,using a system based on that of Bretag et al. (1995). Aseverity rating (0: no disease, 1: 1−20% stem area girdled;2: 20−40% stem area girdled; 3: 40−60% stem area girdled;

4: 60−80% stem area girdled; 5:=>80% stem area girdled)was recorded for each of the first 10 internodes on 30 stems ineach plot, and the mean calculated.

Data on ascochyta blight severity

AB severity was analysed in relation to time of sowing offield pea. This analysis included only the experimental datawhere no disease control measure was employed, except forthe cases when the effect of fungicide treatment wasinvestigated. To analyse the disease severity across WesternAustralia, altogether 482 observations (average of 30 plantsamples per plot) from 18 experiments described above,were used. Sowing times ranged from 3 May (day of theyear (DOY) 124) to 16 July (DOY 198). Data-points wereaveraged across consecutive weeks of time of sowing,

Table 1 List of experiments (showing code, location, year, varietyand time of sowing), the sources of data for meta-analysis of severityand yield loss from ascochyta blight (AB) disease on field pea in

Western Australia. The data source code is typical experimental codeused by the Department of Agriculture and Food Western Australia(DAFWA) to archive data in the system

Region/locationa Data source (DAFWA experimental code) time of sowing (as day of the year)

1989 1996 1997 1998 1999 2002 2003 2005

North 97WH33bcd

Kalannie 124, 150m

Moora Coomberdale 89MO40be

157, 171, 181

CWBl -West 89A20be 97AD27bcd

Avondale RS 133, 157, 171 133m, 142

East Beverly Annex 89EB21be

145, 160,170

Northam CCS 96AD15bf

134, 141, 163

CWBl-East 96ME15bcd

Bruce Rock 134m, 141m, 163m

Merredin RS 89M50be 97ME59bcd 05NO05bk

139, 153 129, 150 126, 140, 151m, 167

Newdegate RS 89LG50be

150, 173, 198

South West 99GS73-132bg

Gnowangerup 134, 152m

Mt Barker RS 98MT18-88bh

149m, 167m, 180m

Nyabing 98GS18bh

135m, 149m, 168

South East 03ED66bj

EDRS 145, 168m

Scaddan 98ES8bh 99ES85-130bg 02PASEbi

135 m, 149m, 166m 154m, 168 m 143, 171, 189

a Geographical location: Avondale RS: 32.07o S and 116.52o E; Bruce Rock: 31.52o S and 118.08o E; East Beverly Annex: 32.01o S and 117.02o E; EDRS:33.36o S and 121.47o E; Gnowangerup: 33.55o S and 117.59o E; Kalannie: 30.21o S and 117.07o E; Northam CCS: 31.38o S and 116.40o E; Merredin RS:31.29o S and 118.17o E; Moora Coomberdale: 30.38o S and 116.00o E; Mt Barker RS: 34.37o S and 117.40o E; Newdegate RS: 33.06o S and 118.50o E;Nyabing: 33.38o S and 118.08o E; Scaddan: 33.26o S and 121.43o E.b Data source (DAFWA experimental code); c Data for fungicide treatments; d Dundale; e Alma, Dundale, Pennant, Wirrega; f Alma, Laura, Wirrega;g King, Laura, Magnet, Parafield, WAP2013, WAP2022; h Dundale, King, Magnet; i Dunwa, Dundale, Helena, Parafield; j Dunwa, Dundale, Helena, Kaspa,Snowpeak; k Kaspa. l CWB: Central wheatbelt; m data for yield loss analyses.

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beginning on 7 May (DOY 128) and ending on 16 July(DOY 198), and the corresponding standard deviation wascalculated. To relate pre-sowing rainfall events to time ofsowing and disease severity, 45 data-points were used. Apre-sowing rainfall event was defined when daily rain,beginning on 1 January for a season, was equal to or greaterthan 1mm. This rainfall amount is assumed to be theminimum threshold for maturation of pseudothecia oninfected field pea stubble as in the case of blackleg oncanola (Salam et al. 2007).

The number of data-points used to analyse the ABseverity for five regions varied as it depended on theavailability of data; with 44, 116, 116, 108 and 118available, respectively, for the North, Central WheatbeltWestern, Central Wheatbelt East, South West and SouthEast regions. The seasonal effect on the severity of thedisease was analysed using a single or multi-location data,as available, within a region. These data were not averagedacross weekly intervals for time of sowing for regional andseasonal analyses of severity of AB.

Data from 36 plots from four experiments were used foreach of three fungicide treatments (no fungicide control,using in-furrow fungicide and fortnightly fungicide sprays)to compare the effect of fungicidal control on AB severity.

For this, data were averaged across consecutive weeks fortime of sowing, beginning on 7 May (DOY 128) andending on 11 June (DOY 163), and the correspondingstandard deviation was calculated.

Data and calculation of yield loss

Plot yield data and the corresponding AB severity from 18experiments were used to calculate yield loss. For this, allthe disease severity ratings (0 to 5, rounded to zero decimalpoint) was sorted for each time of sowing for each varietyfor each experiment. When there was only one rating ofdisease severity for a time of sowing, data were excludedfrom this calculation. In most cases, at least two plot yielddata were available for each rating of disease severity; ifnot, data for this disease severity were not considered. Yieldloss was calculated as follows:

YLTOSXVYEXPZ ¼ MPYBSA �MPYBSLð Þ= MPYBSLð Þ»100

Where, YLTOSXVYEXPZ is the yield loss, expressed aspercentage, for “X” time of sowing (TOS) with variety “Y”in experiment “Z”, MPYBSA is the maximum of the plotyields (t ha−1) in the AB severity class A (A=1, 2 … 5) and

Fig. 1 Map of the south–west-ern agricultural regions ofWestern Australia showing thefive regions from which data hasbeen collected

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MPYBSL is the maximum of the plot yields (t ha−1) in thelowest AB severity class for the same time of sowing withthe same variety in the same experiment. From this, 67data-points for yield loss were derived, which varied overthe disease severity range of 0.15 to 4.93. These included30 data-points for Dundale, 19 for Magnet, 13 for King and1 each for Kaspa, Laura, Parafield, WAP2013 andWAP2022.

To analyse the yield across Western Australia, a total of67 derived data-points averaged on each disease severityclass and the corresponding standard deviation for yieldloss and disease severity class were calculated. Allavailable data-points specific to a variety were used toanalyse varietal effect on yield loss. To analyse the effect ofregion and season-finishing conditions on yield, 48 data-points were considered which excluded data-points forMagnet. The variety Magnet was omitted from the analysesof regional and seasonal effect on yield loss, because itshowed significantly higher yield loss compared to all othervarieties, whereas the other varieties did not differsignificantly to each other (see comments in Resultssection). There were 4, 6, 9, 17 and 12, respectively, forthe North, CWB-W, CWB-E, South West and South Eastregions. To analyse the effect of season-finishing conditionon disease severity, a season was defined as “dry finishing”or “wet-finishing”. A season was arbitrarily termed, for thisstudy, as wet finishing when the number of rainfall events(above 1 mm per day) during September to mid-Octoberexceeded 6, otherwise it was termed as dry finishing. There

were 22 and 26 data-points available, respectively, for dryand wet finishing seasons.

Data analysis

A simple linear regression technique was used to quantifythe severity of AB across time of sowing, and the yield lossfrom AB across the disease severity. For yield lossanalyses, the intercept of regression equations was set to 0(Bretag et al. 1995). The relationship between the time ofsowing and disease severity from the fortnightly spraytreatment was non-linear. Therefore, to compare this toother similar treatments, data were transformed (squareroot) to derive simple linear regressions. Intercepts and/orslopes of the regressions lines were compared using pairedt-tests. Note that due to data scarcity comparison of allseasons was not possible across all the regions.

Results

Severity of ascochyta blight on field pea where fungicideswere not applied

Under natural field conditions where fungicides were notapplied, the severity of AB, across Western Australiangrain-belt, was projected to reach its maximum limit (ABseverity 5) during mid-April and linearly declined to almostnil by mid-July (Fig. 2). The relationship between ABseverity and weekly time of sowing, quantified by a simple

0

1

2

3

4

5

16-Apr

23-Apr

30-Apr

7-May

14-May

21-May

28-May

4-Jun

11-Jun

18-Jun

25-Jun

2-Jul

9-Jul

16-Jul

Time of sowing

Sev

erit

y o

f asc

och

yta

blig

ht

Fig. 2 The influence of time of sowing on ascochyta blight (AB)severity, showing the negative correlation and its linear regression (Y=10.75–0.054*TOS, SE=0.008, P≤0.001, R2=0.80, n=11, RSE=0.62.TOS is the time of sowing as day of the year; SE is the standard error, Pis the statistical significance, R2 represents adjusted R2, n is the numberof observations, and RSE is the residual SE). Filled circle with verticalline represent weekly average and standard deviation, respectively

0

10

20

30

40

23-Apr

30-Apr

7-May

14-May

21-May

28-May

4-Jun

11-Jun

18-Jun

25-Jun

2-Jul

9-Jul

16-Jul

Time of sowing

Nu

mb

er o

f p

re-s

ow

ing

rai

nfa

ll ev

ents

Fig. 3 The number of pre-seasonal rainfall events, a major determinantof ascochyta blight (AB) disease severity, has a positive correlation withthe time of sowing. It is represented by the linear regression, Y=−20.42+0.275*TOS, SE=0.047, P≤0.001, R2=0.43, n=45, RSE=5.79 (TOS isthe time of sowing as day of the year; SE is the standard error, P is thestatistical significance, R2 represents adjusted R2, n is the number ofobservations, and RSE is the residual SE). Filled circle represents thepre-sowing rainfall event (when daily rain=>1 mm)

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regression line, was significant (P<0.001, R2=0.80, n=11).The AB severity varied, in most cases widely, on eachcalculated step of weekly time of sowing (shown asstandard deviation, Fig. 2). The pre-sowing rainfall events,a major determinant of availability of AB ascospores,increased linearly and significantly (P<0.001, R2=0.43, n=45, Fig. 3) with delayed time of sowing. However, a similarnumber of rainfall events occurred along a wide range oftime of sowing. For example, a number of sowings betweenlate-April and mid-June received 15 events of pre-sowingrainfall. The pre-sowing rainfall events had a significanteffect on AB severity (P<0.001, R2=0.28, n=45, Fig. 4):the more the events occurred, the less was the diseaseseverity.

The statistics of simple regression analyses for theresponse of AB severity in relation to time of sowing, forfive agricultural regions and the seasons within each region,is shown in Table 2. When t-tests were performed, no

Table 2 Statistics of simple linear regression equations relating severity of ascochyta blight (AB) disease on field pea to time of sowing of thecrop in five regions of Western Australia across growing seasons (i.e., years)

Region/season Intercept SEia Slope SEsb Pc (<=) R2d ne RSSf Time of sowing (DOYg)

from to

North

1989 8.63 0.36 −0.05 0.002 0.001 0.93 36 0.19 146 181

1997 12.81 2.65 −0.06 0.019 0.05 0.59 8 0.71 124 150

All North 13.01 0.87 −0.07 0.005 0.001 0.80 44 0.64 124 181

CWB-Wh

1989 6.93 1.40 −0.03 0.009 0.001 0.15 72 0.84 145 181

1996 3.34 0.27 0.00 0.002 ns 0.05 36 0.14 134 163

1997 15.53 5.39 −0.09 0.039 0.06 0.37 8 0.50 133 142

All CWB-Wh 8.19 0.73 −0.04 0.005 0.001 0.36 116 0.75 133 181

CWB-Ei

1989 3.47 0.38 −0.02 0.002 0.001 0.52 60 0.38 130 198

1996 1.95 0.75 0.01 0.005 ns 0.00 40 0.39 134 163

1997 10.59 0.75 −0.05 0.021 0.05 0.24 16 0.67 129 150

All CWB-Ei 8.68 0.89 −0.05 0.006 0.001 0.35 116 1.18 129 198

SWj

1998 2.60 0.80 0.00 0.005 ns 0.00 72 0.60 135 180

1999 13.54 1.60 −0.08 0.011 0.001 0.56 0.36 0.64 134 152

All SWj 6.23 0.76 −0.03 0.005 0.001 0.21 108 0.78 134 180

SEk

1998 8.09 1.61 −0.04 0.011 0.001 0.22 39 0.81 135 166

1999 4.49 1.24 −0.02 0.008 0.01 0.13 36 0.32 154 168

2002 12.49 1.63 −0.06 0.009 0.001 0.75 16 0.70 143 189

2003 12.91 1.33 −0.07 0.008 0.001 0.69 30 0.53 145 168

All SEk 10.92 0.76 −0.06 0.005 0.001 0.54 118 0.73 135 189

WAl 10.68 1.39 −0.05 0.009 0.001 0.79 11 0.65 124 198

a Standard error of intercept; b Standard error of slope; c Statistical significance of regression equation;d Represents adjusted R2 ; e Number of observationf Residual standard error; g Day of the year; h Central Wheatbelt West; i Central Wheatbelt East; j South West; k South East; l Western Australia

0

1

2

3

4

5

0 10 20 30 40

Number of pre-sowing rainfall events

Sev

erit

y o

f asc

och

yta

blig

ht

Fig. 4 The influence of the number of pre-sowing rainfall events onascochyta blight (AB) severity. Each rainfall event contributes to thematuration and release of ascospores, resulting in a generally negativecorrelation. This relationship is represented by the linear regression, Y=4.61–0.09*TOS, SE=0.022, P≤0.001, R2=0.28, n=45, RSE=1.13(TOS is the time of sowing as day of the year; SE is the standarderror, P is the statistical significance, R2 represents adjusted R2, n is thenumber of observations, and RSE is the residual SE). Filled circlerepresents the severity of ascochyta blight on field pea

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significant differences (P≤0.05) were observed between theslopes of any pairs of the simple regression lines. However,in most of the cases differences between intercepts weresignificant (Table 3). The intercept, which may be consid-ered equivalent to the initial AB severity, was significantlyhigher in 1997 than 1989 in the North and CentralWheatbelt East (CWB-East), but not in the Central Wheat-belt West (CWB-West). The status of initial AB severitywas significantly lower in 1996 than 1997 in both CWB-East and CWB-West; on the other hand, the disease statuswas also lower in 1996 compared to 1989 in both theregions, but it was not statistically significant in CWB-East(Tables 2 and 3). In 1999, the status of initial AB severitywas significantly higher in the South West compared to1998, but not in the South East region. In the South East,the lowest initial AB severity identified in 1999 wassignificantly different to 2002 and 2003, whereas the initialAB severity in 2002 and 2003 were statistically similar(Tables 2 and 3). When a single season was comparedbetween the regions, it was observed that the initial ABseverity was not significantly different in 1989 between theNorth and CWB-West, but was significant between theNorth and CWB-East and also between CWB-East andCWB-West (Table 3). In 1996, this difference was

statistically significant between CWB-East and CWB-West. The initial AB severity in 1997 was statisticallysimilar in the North, CWB-East and CWB-West. Both in1998 and in 1999, the initial AB severity significantlyvaried between South East and South West.

When the analysis was performed between the regionsacross the seasons, it was identified that the North had thehighest initial AB severity, statistically similar to South Eastbut significantly different from the rest other regions

Region/season Comparison Significance (P<=0.05)

Between the seasons within a region

North 1989 and 1997 *

CWB-Wa 1989 and 1996 *

1989 and 1997 ns

1996 and 1997 *

CWB-Eb 1989 and 1996 ns

1989 and 1997 *

1996 and 1997 *

South West 1998 and 1999 *

South East 1998 and 1999 ns

1998 and 2002 ns

1998 and 2003 *

1999 and 2002 *

1999 and 2003 *

2002 and 2003 ns

Between the regions within a season

1989 N and CWB-Wa ns

N and CWB-Eb *

CWB-Wa and CWB-Eb *

1996 CWB-Wa and CWB-Eb ns

1997 Nc and CWB-Wa ns

Nc and CWB-Eb ns

CWB-Wa and CWB-Eb ns

1998 South West and South East *

1999 South West and South East *

Table 3 The test of significance(t-test, P≤0.05 (* denotes forsignificant and ns denotes fornot significant)) of intercepts ofthe simple regression equationspredicting ascochyta blight (AB)disease severity on field peaover time of sowing affected byseasons (i.e., years) within aregion, and regions within aseason in Western Australia

a Central Wheatbelt West; b Cen-tral Wheatbelt East; c North

Table 4 Test of significance (t-test, P≤0.05(* denotes for significantand ns denotes for not significant)) of intercepts of the simpleregression equations predicting ascochyta blight (AB) disease severityon field pea over time of sowing across five regions and the state ofWestern Australia

Region/state Northa CWB-Wb CWB-Eb SWd SEe

CWB-Wb *

CWB-Ec * ns

SWd * ns *

SEe ns * ns *

WAf ns ns ns * ns

a North; b Central Wheatbelt West; c Central Wheatbelt East; d South West;e South East; f Western Australia

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(CWB-East, CWB-West and South West) (Tables 1 and 4).This difference between South East and two other regions,CWB-West and South West was also significant. Pooleddata for all seasons and regions show that South West hadthe significantly lowest initial AB severity compared toother four regions (Table 4).

Severity of ascochyta blight on field pea where fungicideshave been applied

Ascochyta blight (AB) severity was compared for differentsowing dates in the period between the first week of Mayand mid-June. The severity of AB remained similar at theearly time of sowing (week ending 7 May) irrespective ofcontrol measures, in-furrow fungicide (AB severity 4.73) orfortnightly fungicide sprays (AB severity 4.25) compared tonil control (AB severity 4.85) (Fig. 5). Neither the intercept(initial AB severity status) nor the slope (rate of change ofdisease severity over time) of the linear regression lines forthe three treatments (in-furrow fungicide, fortnightly fungi-

cide sprayed and nil control treatment) were statisticallydifferent to each other. The response of severity in thefortnightly spray treatment to time of sowing, however,could better be represented in an exponential equation(Fig. 5) indicating a sharp decrease in AB severity frommid-May onward compared to earlier sowings.

Yield loss from ascochyta blight severity

The percentage yield loss, across the varieties and field peagrowing regions, when summarised for each AB severityrating (1–5) and regressed through the origin, was found tobe linear. This linear relationship was statistically signifi-cant (P≤0.001, SE=1.14, R2=0.70). The yield loss due toAB disease was calculated as 10.27% per unit of ABseverity rating, indicating about 51% loss could occur withthe highest disease severity (Fig. 6). However, increasingvariability was observed in data of yield loss as the diseaseseverity increased.

Among the varieties the yield loss from Magnet wassignificantly (P≤0.05) highest, 13.0% per unit of ABseverity rating (P≤0.001, SE=1.67, R2=0.72, n=19), whilethe 8.8% yield loss (per unit of AB severity rating) inDundale (P≤0.001, SE=0.72, R2=0.80,n=30) was statisti-cally similar to the 7.2% yield loss (per unit of AB severityrating) in King and losses in the other varieties (P≤0.001,SE=1.39, R2=0.55, n=18) (Table 5). This differencebetween Magnet compared to all other varieties wassignificant (P≤0.05), whereas Dundale and King and othervarieties did not differ significantly to each other. Conse-quently, the analyses of regional and seasonal effect onyield loss did not include Magnet. There were numericalvariations, but statistically insignificant, in yield loss

0

1

2

3

4

5

30-Apr 7-May 14-May 21-May 28-May 4-Jun 11-Jun 18-Jun

Time of sowing

Sev

erit

y o

f as

coch

yta

blig

ht

Fig. 5 Interactions between time of sowing and fungicidal control onascochyta blight (AB) disease severity. These relationships are generallylinear, negative correlations, with the exception of treatment 4, afortnightly fungicidal spray, which is better represented exponentially.Open (1996) and filled (1997) circles are weekly averages of AB severityfor no chemical treatment (top line) represented by the linear regression,Y=9.96–0.046*TOS, SE=0.014, P≤0.01, R2=0.22, n=36, RSE=0.94.Open (1996) and filled (1997) squares are weekly averages of ABseverity for the treatment that was treated with Impact® in-furrow(flutriafol) (Line second from top) represented by the linear regression,Y=10.19–0.053*TOS, SE=0.170, P≤0.01, R2=0.20, n=36, RSE=1.17.Open (1996) and filled (1997) triangles are weekly averages of ABseverity for fortnight spray with Bravo (chlorothalonil) represented bythe linear regression (Line second from bottom), Y=6.29–0.038*TOS,SE=0.005, P≤0.001, R2=0.63, n=36, RSE=0.34, and exponential(bottom line) regression,Y=106,408 * Exp(−0.086*TOS), R2=0.66.For all the regression lines, as applicable, TOS is the time of sowing asday of the year, SE is the standard error,P is the statistical significance, R2

represents adjusted R2, n is the number of observations, and RSE is theresidual SE). In the figure, vertical lines represent standard deviations.Values in the figure show untransformed (i.e. raw) data. Note, data werenot available for all times of sowing in both experimental years

0

20

40

60

80

0 1 2 3 4 5

Severity of ascochyta blight on field pea

Per

cen

tag

e o

f yi

eld

lo

ss

Fig. 6 The severity of ascochyta blight (AB) has a positive correlationwith the percentage of yield loss on field pea. This relationship isrepresented by the linear regression, Y=10.27*DS, SE=1.14, P≤0.001,R2=0.70, n=5, RSE=8.42 (DS is the disease severity rating on 0–5scale, where 0 is no disease and 5 being the highest, SE is the standarderror, P is the statistical significance, R2 represents adjusted R2, n is thenumber of observations, and RSE is the residual SE). Filled circlerepresents the percentage of yield loss and horizontal and vertical linesrepresent standard deviations

598 M.U. Salam et al.

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between the regions, with the highest (9.7% per unit of ABseverity rating) being in the North and the lowest in theCentral Wheatbelt West (CWB-West) (Table 5). On theother hand, yield loss significantly (P≤0.001) varieddepending on the end of season environmental conditionssuch that a dry finishing season resulted in higher yield loss(10.1% per unit of AB severity rating) than a wet finishingseason (6.6% per unit of AB severity rating).

Discussion

Rosenberg et al. (2004) state research falls into twocategories: primary research, in which an investigatorexamines a particular phenomenon, and research synthesis,in which an investigator reviews and summarises primaryresearch. Meta-analysis, in a broader sense, is a quantitativereview of the primary research (Glass 1976). In this study,we have synthesised the results of 18 field experimentsconducted over eight seasons in 13 locations in WesternAustralia to address and quantify the important issuesrelated to management of AB disease in field pea.

Firstly, we examined the effect of time of sowing on theseverity of AB disease on field pea. The severity of ABacross south-western region of Western Australia wasprojected to reach its maximum limit when a crop wassown during mid-April and linearly declined to almost nil

for a crop sown in mid-July. The result from this studyagrees with a number of studies conducted in south-easternAustralia showing that early sown crops are more likely tobe severely infected by the disease than later sown crops(Bretag et al. 2000; Davidson and Ramsey 2000). Thestrength of our study is that it provides, in turn, anestimation of AB severity, offering a basis for makingstrategic decisions to identify safer times of sowing for fieldpea crops in farming systems. While there were regionaland seasonal variations in the scale of severity, the trendwas more or less similar in relation to the time of sowing ofthe crop across seasons and regions. Our analyses show thatthere are opportunities to sow field pea crops early in thesowing window in some locations and in some years, andthis information can be utilised through the development ofdisease forecasting systems, such as those for the blacklegpathogen (Leptosphaeria maculans) in canola (Salam et al.2003b, 2007).

Secondly, we determined the extent to which the severityof the disease can be controlled by using fungicides. WhileAB can be managed by delaying the time of sowing of fieldpea, this can incur an agronomic yield penalty (McMurrayet al. 2011). For this reason, a strategy has been proposed tocombine early sowing with strategic application of fungi-cide (McDonald and Peck 2009). However, the analysisfrom our study shows that under existing disease pressure,the severity of AB remains similar at the early time of

Table 5 Statistics of simple linear regression equations relating yield loss to severity of ascochyta blight (AB) disease on field pea as affected bycrop varieties and growing regions of Western Australia

Variety/region/season Slope SEa Pb (<=) R2c nd RSSe Ascochyta blight severity

from to

Varietyf

Dundale 8.79 0.72 0.001 0.80 30 10.83 0.15 4.60

Magnet 12.99 1.67 0.001 0.72 19 17.87 0.85 4.51

King and othersi 7.19 1.39 0.001 0.55 18 14.30 0.57 4.93

Regiong

North 9.70 1.50 0.05 0.60 4 8.90 0.15 4.60

Central Wheatbelt West 6.88 0.67 0.001 0.75 6 5.25 1.94 4.22

Central Wheatbelt East 9.12 0.77 0.001 0.82 9 4.85 0.57 2.97

South West 8.76 1.71 0.001 0.56 17 16.60 0.57 3.36

South East 7.86 1.28 0.001 0.68 12 12.93 1.60 4.93

Season finishingh

Dry 10.09 1.13 0.001 0.74 22 14.22 0.15 0.57

Wet 6.63 0.63 0.001 0.77 26 8.55 4.93 4.22

a Standard error of the slope; b Statistical significance of regression equation; c Represents adjusted R2 ; d Number of observation; e Residual standard errorf Statistical significance for variety (t-test, P≤0.05): Magnet significantly different to Dundale and to King and other varieties; Dundale and King and othervarieties not significantly different to each otherg Statistical significance for region (t-test, P≤0.05): No significant differences between regionsh Statistical significance for season finishing (t-test, P≤0.05): Dry season significantly different to seasoni Include Kaspa, Laura, Parafield, WAP2013 and WAP2022

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sowing (first week of May) irrespective of control meas-ures, viz. in-furrow fungicide application, or fortnightlyfungicide sprays, or nil control. Further, a recent study(McMurray et al. 2011) in South Australia largely confirmsfindings that are similar to our results.

Thirdly, we determined what yield loss from AB can beexpected in response to varying intensities of diseaseseverity. We quantified the yield loss due to AB as 10.3%per unit of AB severity rating (0 to 5 scale), indicating lossof ≥50% could occur, as was observed with the highest ABseverity in these experiments. This result is comparable withfield studies conducted in Victoria (Bretag et al. 1995).Furthermore, our study reveals an interesting relationshipbetween seasonal conditions and yield loss due to thedisease. As has often been perceived by the scientists (W.J.MacLeod and M. Seymour, personal communications), a dryfinish to the season results in significantly higher yield lossesthan a wet finish due to the combination of there being lessavailable soil water content, and a decreased ability of plantfoliage to access water due to stem damage caused by AB.

Using the results from this study, both the yield loss and/or the yield advantage that may be expected from choosinga particular time of sowing for field pea can be quantified.This will help in strategic management of the disease underWestern Australian faming systems.

Acknowledgements We thank the Australian Grains Research andDevelopment Corporation (GRDC) for financial assistance in thiswork. The last author acknowledges DAFWA for financial support ofhis salary and research. Authors are highly grateful to one of theanonymous reviewers who patiently went through the manuscript andpassed useful comments for its improvement.

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