Prediction of yield loss caused by Orobanche spp. in carrot and pea crops based on the soil seedbank

Prediction of yield loss caused by Orobanche spp. in carrot

and pea crops based on the soil seedbank

R. H. BERNHARD, J. E. JENSENAND C. ANDREASEN

Department of Agricultural Sciences, The RoyalVeterinary and Agricultural University,Thorvaldsensvej 40, DK-1871 Frederiksberg C,

Denmark

Received 20 June 1997

Revised version accepted 23 January 1998

Summary

Root parasites of the genus Orobanche causeserious losses in many subtropical crops. Direct

control options are very limited and crop yieldloss can reach 100%. Prediction of potentialdamage in a crop before sowing or planting

would support farmers in their choice of crop.This paper discusses the relationship between thenumber of Orobanche spp. seeds in the ®eld andyield loss in peas (Pisum sativum L.) and carrots

(Daucus carota L.) in Israel. Yield loss due toOrobanche crenata Forsk. in peas was 100% athigh infestations, whereas in carrots when

O. crenata and O. aegyptiaca were present itstabilized at about 50% for moderate infesta-tions of 200 seeds per kg of soil. Statistical ana-

lyses related the yield loss from parasitism in peasand carrots to the numbers of Orobanche seedsremaining in the soil. A rectangular hyperbolic

model, previously applied to competition data,®tted the data well. Con®dence intervals for percent yield loss were calculated using the boot-strap method. The practical applications of these

models in predicting yield loss are discussed.

Introduction

Orobanche spp. (the broomrapes) are trouble-some root parasites that depend completely on a

host to complete their life cycle (Ismael & Obeid,1976; Press et al., 1986). Some species have a

wide host range (e.g. O. aegyptiaca Pers.), othersa narrow one (e.g. O. cumana Wallr.) (Chater &Webb, 1972; Parker & Wilson, 1986). Many are

major pests of predominantly dicotyledonouscrops in warm and dry areas (ter Borg, 1986),and the majority of species is found in the

Mediterranean region (Musselman, 1986).Depending on the level of infestation, damage

from Orobanche spp. can range from zero tocomplete crop failure by causing wilting of leaves

and stunted growth (Singh et al., 1971). Themost serious losses occur in species within thefamilies Solanaceae, Fabaceae, Apiaceae and

Asteraceae (Mazaheri et al., 1991). Some verydamaging species occur in Israel, where farmersare not always aware of an infestation and often

possess limited knowledge of potential crop loss.In heavily infested ®elds, farmers are sometimesforced to give up growing crops susceptible to

Orobanche spp. because of the high yield losses.Economically e�ective means of controlling Or-obanche spp. are limited at present (Sauerborn,1991; Garcia-Torres, 1994), and therefore other

options of diminishing the problem need to beconsidered.

Prediction of potential Orobanche spp. dam-

age in a crop before sowing or planting wouldhelp the farmer decide whether to grow theoriginally planned crop, or to substitute it with

another less or non-susceptible crop, althoughthe latter may be less pro®table under parasite-free conditions. Prediction of Orobanche spp.damage may be complicated, however, by dif-

ferences in host range, season of activity, via-bility and germination of parasite seeds betweenand within the various species (Dalela &

Mathur, 1971; Chater & Webb, 1972), as well asby heterogeneous distribution of seeds in the®eld.

Weed Research, 1998, Volume 38, 191±197

Ó 1998 European Weed Research Society

A correlation between the number of para-

sites in a ®eld and yield reduction has been found(Linke et al., 1991a; Zaitoun et al., 1991). Linkeet al. (1991a) also established a positive correla-

tion between the number of emerged shoots andOrobanche crenata seed density, depending onplanting date, and a negative correlation be-tween the number of seeds and the yield of ®eld

beans, in both pot and ®eld experiments. How-ever, Linke et al. (1991b) found that the rela-tionship between seedbank and numbers of

Orobanche plants emerged in lentil (Lens cul-inaris L.) depended on the time of planting.

A method of predicting the potential Or-

obanche spp. damage to a crop has not beendeveloped. One method of predicting cropdamage in terms of yield reduction could bebased on a quantitative determination of Or-

obanche spp. seeds in the soil, coupled with anestimation of their viability. The latter is notfeasible, however, as viability tests are very la-

borious and results cannot be obtained within areasonable time. Under the hypothesis that thereis a relationship between the total Orobanche

spp. soil seedbank and the number of germinat-ing seeds in a sensitive crop, a model based ex-clusively on the number of seeds should be

su�cient. Although such a model would notexplain the di�erences in emerged Orobancheplants because of climatic and other factors, itmay still give reasonable predictions. However,

when we understand better the e�ects of thesefactors on the emergence, we should be able tofurther improve the predictions.

The aim of this paper is to show that thenumber of Orobanche spp. seeds found in soilsamples can be used to predict the actual yield

(or per cent yield loss) in some carrot (Daucuscarota L.) and pea (Pisum sativum L.) ®elds. Thiscould be a step towards enabling farmers tomake predictions about future problems before

crop sowing.

Materials and methods

Sampling sites

Surveys were conducted in the spring of 1994 ofthree carrot ®elds in the Bet Shean valley in Is-rael, and one pea ®eld in the south-western part

of the country, at the end of the growing season.One carrot ®eld was located at Kibbutz TiratTsvi, and two at Kibbutz Sheluhot; the pea ®eld

was located at Kibbutz Yad Mordechai. The

populations of Orobanche spp. had not yet dis-persed their seeds.

In the carrot ®eld at Kibbutz Tirat Tsvi (®eld

`T'), O. aegyptiaca was the dominating species,whereas in the two other carrot ®elds, KibbutzSheluhot (®eld `S' and ®eld `SH'), O. crenataForsk. was predominant, with no signi®cant

occurrence of O. aegyptiaca. O. crenata was theonly species in the pea ®eld at Kibbutz YadMordechai (`®eld YM'). Further ®eld data are

presented in Table 1. All ®elds were cultivatedaccording to normal local practice using modernmachinery. There was no attempt to control

weeds, either before or during the growing sea-son, in any of the ®elds.

Sampling method

Sampling was conducted prior to commercialharvest and prior to Orobancheseed ripening

and shattering.

Carrots. The crop was grown in beds containingfour rows. The distance between rows was

0.38 m, and the carrot seeds were sown in a stripapproximately 8 cm wide. The bed was 1.93 mwide. The sampling area (2.00 m ´ 0.965 m)covered two rows: one outer and one inner row.

Individual plots with varying degrees of infesta-tion were selected to obtain variation in the Or-obanche spp. seedbank, based on visual

observations of the density of emerged parasiteplants. In each plot the number of emerged Or-obanche spp. plants was determined, and a soil

sample of 1.5±2.0 kg, comprising 3±4 subsam-ples, was taken between the rows. The soil wassampled with a spoon to a depth of approxi-

mately 20 cm. Crop debris was scraped o� be-

Table 1. Field and crop information

Crop and Date of Date of Soil clayField cultivar sowing harvest content (%)

S Carrots, 13/10/93 20/3/94 40Peridor

SH Carrots, 06/9/93 24/3/94 30Nanco

T Carrots, ± 03/3/94 30±40*Peridor

YM Peas,Boomer

13/12/93 12/4/94 17

*Estimated; ±, data not collected.

192 R.H. Bernhard et al.

Ó 1998 European Weed Research Society, Weed Research 38, 191±197

fore the soil sample was taken, in order to fa-

cilitate the counting of Orobanche spp. seeds.The carrots were subsequently harvested,cleaned, weighed and counted.

Peas. The crop was sown at a rate of about 90seeds m)2, producing about 80 pea plants m)2.

Each 2.00 m ´ 1.00 m plot was selected accord-ing to the same criteria as for the carrots. Withinthe plots, O. crenata plants were counted and theentire pea plants harvested. The plant material

was stored in a cold room and threshed andweighed the following day. Approximately 5 kgof soil was taken from the topmost 20 cm and

thoroughly mixed, and 2.5 kg was analysed. Allsoil samples were subsequently air-dried in agreenhouse, and screened to remove stones and

soil debris larger than 2.5 mm.

Elutriation procedure

A method for separation of Striga spp. seedsfrom soil is described by Eplee et al. (1985) andEplee & Norris (1990). Eplee et al. (1985) sug-

gested the same procedure for seeds of Or-obanche spp. Because of the high clay content ofthese soils (Table 1), this method was not di-

rectly applicable to Orobanche spp. The proce-dure was therefore modi®ed as follows.

A subsample of 200 g was taken from each

soil sample. After thoroughly mixing it with 10 gof sodium carbonate and water up to 1.5 L, thesamples were elutriated according to the proce-

dure described by Eplee et al. (1985) and Eplee &Norris (1990), except that when the water supplywas turned on, the sample was immediatelypoured into the cone of the elutriator. Water

pressure of the incoming water was 550 kPa,giving 30 L min)1. The soil-removing spray be-low the collection sieve operates with 3 L min)1,

resulting in only one correct position of theopening of the under¯ow outlet because the wa-ter level in the cone should travel the last 10 cm in

15 s. The under¯ow outlet was opened when thewater level was 10 cm below the rim of the cone.The elutriation cycle was terminated after 4 min.

The separation column

An important alteration to the method described

by Eplee & Norris (1990) is that no materiallighter than Orobanche spp. seeds can be dis-carded because a signi®cant proportion of the

viable seeds from the naturally infested soil ¯oat.

Hence all material above the salt solution phasewas drained on to examination screens (100-lmopenings).

Quantitative determination

The material was spread on to a suitable number

of other screens identical to the collection screenand air-dried on absorption paper. Counts ofOrobanche spp. seeds were carried out under a

dissecting microscope at 18±40 ´ magni®cation.The counting was facilitated by a ruled piece ofpaper placed under the examination screen, di-

viding it into 14 smaller areas.

Data

Data from 23 plots within the pea ®eld wereavailable. As only one ®eld was included in theinvestigation, no information on the variation

between ®elds was available. For each plot, thefollowing variables were recorded: number ofOrobanche spp. plants m)2, number of Or-

obanche spp. seeds in the soil kg)1 and the peayield in g m)2. The plant densities of the cropwere not recorded, and were considered constant

in the statistical analyses.Carrot data were collected from three ®elds,

with 18, 22 and 19 plots, respectively, giving atotal of 59 observations. The data included the

variables mentioned above plus ®eld identi®cat-ion and the number of carrot plants m)2. As aconsequence, possible variability between ®elds

and variation due to differences in crop age anddensity for the carrot data can be taken intoaccount.

Statistical methods

Models. A biologically sound mathematical

model for describing the e�ect on a crop of aparasitic weed such as Orobanche spp. would benon-linear and should have the following char-

acteristics: (a) decreasing crop yield in responseto an increasing number of parasites; (b) forlarge infestations, crop yield should tend towards

an asymptote; (c) crop yield is non-negative; (d)parameters in the model should have conspicu-ous biological interpretations; and (e) it should

be possible to take into account the e�ect of®elds, crops density etc.

Cousens (1985) suggested a non-linear modelto describe the e�ects of weed density on crop

Yield loss caused by Orobanche spp. 193


yield. The model (eqn 1) describes per cent yield

loss, z, as a function of the weed density, x, usinga rectangular hyperbolic relationship:

z � Bx1� Bx

A

�1�

where A is the maximum (asymptotic) yield lossin per cent (A £ 100) and B describes initial %yield loss when weeds become present (slope at

very low density). Instead of yield loss, we canexpress the relationship between yield, y, andnumber of seeds of the parasite, x, as in eqn (2):

y � C 1ÿ z100

� �� C 1ÿ Bx

100 1� BxA

ÿ � !�2�

where C is the weed-free yield. This model sat-

is®es the criteria outlined above.The model can be extended in several ways to

take into account the density of the crop, the

relative time of emergence of the weeds, andother important factors, as discussed by Cousenset al. (1987). The e�ect of ®elds was included as a

multiplicative e�ect through the C parameter:

y � Cf 1ÿ Bx100 1� Bx

A

ÿ � !�3�

where Cf denotes the weed-free yield of ®eld f.In the following we test the hypothesis that

eqn (3), originally developed to describe the ef-

fects of competition, can be used to predict yieldin carrot and peas parasitized by Orobanche spp.

Transformations. All models ®tted to the data

were checked for variance homogeneity. Thetransform-both-sides (TBS) transformation sug-gested by Carroll & Ruppert (1988) was usedwhen variance was not constant. The power

transformation was used with a parameter k,enabling di�erent degrees of transformation(Box & Cox, 1964).

Con®dence intervals. Having determined thenumber of Orobanche spp. seeds in the soilsamples, it is necessary to estimate the expected

crop yield loss and its con®dence interval toassist farmers inmaking decisions. The non-linearstatistical model does not provide prediction in-tervals at all levels of infestation. Consequently,

the computer-intensive `bootstrap' method (Ef-ron, 1982; Jensen, 1995) was used to constructprediction intervals. For each bootstrap sample,

the parameters were estimated and a predicted

yield loss curve was calculated. The bootstrapwas replicated 5000 times, and 95% con®denceintervals were obtained by using the 2.5% and

97.5% fractiles from the population of predictedyield loss curves.

Results

Correlations between seed and plant numbers

Figure 1 shows the relationships between recov-

ered Orobanche seed numbers from the soilsamples and adult parasite populations. For

Fig. 1. Correlations between seed number in soil samples andactual plant numbers for pea (a) and carrot ®elds, with dif-ferent symbols for ®elds/Orobanche species: s, denotes Kib-butz Tirat Tsvi with O. aegyptiaca; ´ Kibbutz Sheluhot/O. crenata and h, Kibbutz Sheluhot/O. crenata (b). Lines in-dicate linear regressions based on log-transformed numbers.For visualization, zero seed counts were set to 0.5. Regressionline estimates (with standard errors in parentheses) were: peas,intercept � 0.72 (0.26), slope � 0.92 (0.08); carrots, inter-cept � )0.02 (0.27), slope � 0.95 (0.09). Correlation coe�-cients based on untransformed and log-transformed numberswere: peas 0.90, 0.93; carrots: 0.79, 0.81. All correlations weresigni®cantly di�erent from zero.



both crops, these ®gures are approximately

proportional, which means that the seed extrac-tion method gives an unbiased prediction of theactual parasite infestation. The apparent higher

variance for low seed numbers is due to log-transformation of low, discrete numbers.

Statistical analyses

The actual numbers of Orobanche spp. plants inthe plots were generally slightly better predictors

of yield loss than the seed counts used here. Forthe ®nal pea model, pseudo-R2 (calculated as theproportion of the total variance explained by the

model) was 0.818 when parasite seeds were usedas the independent variable, whereas with actualparasite numbers it was 0.821. The carrot modelsgave ®gures of 0.792 and 0.822 respectively.

From a practical point of view, the two differentpredictors gave the same results, with similarmaximum yield loss at high infestations.

Peas. In Fig. 2, the pea yield data are plottedagainst the number of O. crenata seeds per kgsoil. High numbers of parasite seeds in the soil

(exceeding 250 seeds kg)1) clearly result in totalcrop failure. Unfortunately, no plots had para-site seed numbers between 125 and 225 kg)1, andtherefore the two observations around 250

seeds kg)1 have a considerable effect on the ®t-ted regression models.

The TBS transformation signi®cantly im-

proved homogeneity of variance for the pea da-ta. The optimum k estimate was around 0.4.Figure 2 shows the ®t of the rectangular hyper-

bolic model (eqn 2) to the pea data. The pointestimate of the A parameter is greater than 100%(although not signi®cantly di�erent from 100%),

probably because there are only two observa-tions with seed numbers higher than 125 kg)1.

More observations with high seed numbers are

needed before reliable predictions of yield losscan be made. On the other hand, such observa-tions might not be of much practical interest

because the crop is virtually lost when parasiteseed counts exceed 100 kg)1.

The con®dence intervals for per cent yield lossare illustrated in Fig. 4. Estimates (standard er-

rors in parentheses) of the parameters in the es-timated yield loss curve are A � 112% (7) andB � 2.9% kg plant)1 (0.9), whereas the weed-

free yield in the pea ®eld was C � 369 g m)2

(40).

Carrots. Figure 3a shows the carrot yields plot-

ted against parasite seed density, with di�erentsymbols for the three ®elds, which have di�erentyield levels when the number of parasite seeds islow. TBS transformation did not signi®cantly

improve the ®t of the model because variancewas already homogeneous. We therefore usedk � 1.0 corresponding to an untransformed

model.

The yield loss model with multiplicative ®elde�ect (eqn 3) ®tted the data well. Signi®cantdi�erences existed between the ®elds because of

di�erent parasite-free yield levels. Parameter es-timates and asymptotic standard errors weremaximum yield loss A � 52% (11), low densityyield loss B � 1.3% kg plant)1 (0.5), and weed-

free yield for ®eld 1 C � 13.3 kg m)2 (0.5).Figure 3b shows the ®t of the yield loss model(eqn 3) after removal of the multiplicative ®eld

effects. Con®dence intervals for crop yield loss incarrots are shown in Fig. 5. With high numbersof Orobanche spp. seeds in the soil, yield losses

stabilize at around 50%.

Discussion

Although actual parasite number was a slightlybetter predictor of yield loss than seed number,

we have chosen to focus on the relationship be-tween recovered Orobanche seeds and yield loss,so that the method can be used for yield loss

predictions prior to sowing, before the parasitehas emerged. The correlation between Orobanchespp. seeds in the soil seedbank and the actual

infestation is high (Fig. 1), and one mighttherefore consider using infestation in the pre-vious years to estimate yield loss in the following

Fig. 2. Pea data. Observed (s) and ®tted (ÐÐ) pea yield ac-cording to the Cousens model (eqn 2) plotted vs. number ofOrobanche crenata seeds kg)1 of soil.



year. However, as susceptibility varies greatlyamong crop species, this approach requires thatcrops are not rotated on a given area. If the

seedbank turnover rate is high, this predictorshould furthermore be adjusted for seed pro-duction and losses.

The objective was to develop a method ofpredicting yield loss whereby evaluations of theOrobanche seedbank at the beginning of the

season can be used as an aid to farmers making

decisions. However, the data set on which themodel is currently based uses evaluations of the

seedbank that were collected mid- to late season.The use of such data can only be justi®ed if theoriginal early season seedbank has not beenconsiderably depleted by the germination that

gave rise to the emerged Orobanche population.With a bulk soil density of approximately1.4 kg L)1 and soil sampling from the upper 20-

cm soil layer, 1 m2 corresponds to 280 kg of soil.Considering that soil samples with about 100seeds kg)1 gave rise to about 100 established

Orobanche plants m)2 (Fig. 1), this implies lessthan 0.5% depletion of the seedbank. Conse-quently, we think that our approach should bejusti®ed, although in retrospective it would have

been better to do the sampling before sowing.The data sets used here are relatively small,

and the conclusions drawn from them should not

be over-emphasized. Practical recommendations,in particular, cannot be based on such data sets.Nevertheless, the results indicate that the rect-

angular hyperbolic model describes the Or-obanche spp. parasitism data well.

If this method is to be used as a routine

method for predicting yield loss, more data from®elds with di�erent crops and di�erent yieldlevels are required. Another requirement is areasonably cheap and quick way of determining

the number of Orobanche spp. seeds in the soil.The soil sampling and seed extraction methodsused to count seed numbers in this study are

laborious, and cannot immediately be used as aroutine method by farmers or advisors. Digitalimage analysis, a rapidly evolving technique,

may make seed identi®cation and counting lesstime and labour consuming in the future.

The `bootstrap' method proved to be useful incalculating the prediction intervals for yield loss

based on soil seed samples because the predic-

Fig. 4. Estimated pea yield loss in per cent (solid curve) and95% bootstrap prediction intervals (dashed curves).

Fig. 5. Estimated carrot yield loss in per cent (solid curve) and95% bootstrap prediction intervals (dashed curves).

Fig. 3. Carrot data. (a) Yield plotted vs. number of Orobanchespp. seeds per kg soil with di�erent symbols for the three ®elds,symbols as in Fig. 1b, and (b) observed (s) and ®tted (ÐÐ)carrot yield (eqn 3) plotted vs. number of Orobanche spp. seedsper kg soil. The e�ect of ®elds has been removed to simplify thepresentation of data in the plot.



tions are clearly more precise in intervals in

which observations of Orobanche spp. seednumbers are concentrated than in intervals withfewer observations (see Figs 4 and 5).

In conclusion, the yield loss model used is abiologically realistic basis for predicting yieldlosses due to Orobanche spp. parasitism becauseit describes the observed yield losses in response

to increasing soil seedbanks satisfactorily.However, we wish to emphasize that the presentstudy is rather small, and more data representing

a broader range of crops, Orobanche species,infestation levels and growing conditions areneeded to establish a general operational model.

Acknowledgements

Thanks are extended to Yoni Gahali, Depart-ment of Vegetable Crops, The Volcanic Center,Bet Dagan, Israel, for technical assistance. We

also thank Dr Reuven Jacobsohn, Departmentof Vegetable Crops, The Volcanic Center, BetDagan, Israel, for hosting Ruben H. Bernhard

during his stay in Israel, and for providing re-search facilities, intellectual input, technical as-sistance and comments for the manuscript. The

project has been supported ®nancially by theDanish Agricultural and Veterinary ResearchCouncil through Dina (Danish InformaticsNetwork in the Agricultural Sciences), and the

project ``Dynamic Plant Competition andThreshold Models for Weeds''.

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Prediction of yield loss caused by Orobanche spp. in carrot and pea crops based on the soil seedbank