HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Grenz, J. H. Articles by Sauerborn, J. Search for Related Content PubMed Articles by Grenz, J. H. Articles by Sauerborn, J. Agricola Articles by Grenz, J. H. Articles by Sauerborn, J. Related Collections Weed Management Other Legumes Pest Management Systems Crop Models

Production Papers

Assessing Strategies for Orobanche sp. Control Using a Combined Seedbank and Competition Model

^a Inst. Plant Prod. and Agroecology in the Tropics and Subtropics (380b), Univ. of Hohenheim, 70593 Stuttgart, Germany
^b Agric. Prod. Syst. Res. Unit, DPI, Toowoomba, Australia

^* Corresponding author (jangrenz{at}uni-hohenheim.de)

Received for publication February 23, 2005.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Infection with the parasitic weed crenate broomrape (Orobanche crenata Forsk.) causes considerable yield losses in legumes grown in Mediterranean climates. Strategies to control the parasite must include the long-term containment of its soil seedbank. This requires a better understanding of seedbank and competition dynamics. Hence, we built a model of crenate broomrape seedbank dynamics and combined it with a model of broad bean (Vicia faba L.)–crenate broomrape competition within the simulation framework of the Agricultural Production Systems Simulator (APSIM). Parasite seed production is modeled as a function of parasite dry weight, which is an output of the competition model. Newly produced seeds are progressively added to the top-most of three vertically organized seed classes and vertically redistributed by tillage. Seed viability loss follows negative exponential functions, with the rate of seed decay depending on soil moisture status. Effects of further external factors, such as temperature, are not yet included. Viable parasite seeds present at sowing of a host crop serve as input to the next run of the competition model. We quantified effects of environment, rotation, tillage, hand-pulling, and combined strategies on parasite seedbank dynamics and broad bean yield through multiseason simulations. Modeled responses compared well with previously reported field observations. Results suggest that only by combining several management approaches, such as delayed sowing, no-till, and hand-pulling, can parasite populations be contained.

Abbreviations: SE, standard error

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
THE PARASITIC WEED crenate broomrape infects broad bean, lentil (Lens culinaris Medik.), pea (Pisum sativum L.), and other crops in Mediterranean environments, causing major yield and quality losses (Sauerborn, 1991; Parker and Riches, 1993). The angiosperm parasite only germinates in the presence of host-specific root exudates (Musselman, 1980). Parasites attach to the vascular system of the host and withdraw water and assimilates, resulting in yield losses of 5 to 100% (Sauerborn, 1991). Individuals can produce more than 200 000 seeds (López-Granádos and García-Torres, 1991) that can remain viable in the soil for 13 yr or longer (Cubero and Moreno, 1979). In spite of considerable research efforts (e.g., Sauerborn et al., 1989; Foy et al., 1989; Jurado-Expósito et al., 1997), the complexity of host–parasite systems has hampered the development of sufficiently effective and practicable control methods. Linke and Saxena (1991) and Pieterse et al. (1994) suggested that combining various control strategies might provide the best prospects for crenate broomrape management. Due to the multitude of management options and the complexity of host–parasite interactions, identification of control strategies resulting in minimal yield losses by means of classical experimentation requires much time, space, and financial resources. Yet, extrapolation of results to other environments than those studied is hardly possible. A process-based simulation model capable of quantifying interactions of management, environment, host, and parasite can help save resources and accelerate the process of control strategy development.

Most existing weed models either simulate population dynamics or crop–weed competition (Kropff and Lotz, 1992; Kropff et al., 1995). Existing models of broomrape population dynamics simulate the parasite life cycle as a sequence of transitions, to which discrete likelihood factors are assigned (Schnell et al., 1996; López-Granádos and García-Torres, 1997; Kebreab and Murdoch, 2001). Neither host–parasite competition nor effects of external driving variables, such as soil moisture, are included. A competition model of broad bean–crenate broom-rape interactions was developed by Manschadi et al. (2001), integrated into the framework of the Agricultural Production Systems Simulator (APSIM) (Manschadi et al., 2003, 2004) and evaluated against independent field data (Grenz, 2004). APSIM-Parasite simulates growth and development of host and parasite as affected by crop genotype, environment, and management during single growing seasons. Since the APSIM framework applies a soil-centered approach to simulate crop rotations, with effects of one crop on another passed on via the soil (McCown et al., 1996; Keating et al., 2003), it offers a potentially suitable environment for simulating weed seedbank dynamics.

Our objective was to provide a method facilitating quantitative assessments of control strategies and thus the identification of best choices for crenate broomrape control at any location. Considering the multitude of possible interactions, we concluded that only a combined modeling approach would be suitable to deliver the desired outcomes.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Data Sources
Quantitative information on most stages of the crenate broom-rape life cycle was compiled from published data (Linke, 1987; López-Granádos and García-Torres, 1991, 1993, 1996; Schnell, 1993; Kebreab and Murdoch, 1999a; Manschadi et al., 2001). We collected additional data on reproduction from field trials investigating effects of sowing date and crenate broomrape infestation on broad bean that were conducted in Adana (Turkey) during 2001–2002 (Grenz et al., 2005). During grain filling and at crop maturity, we harvested eight individuals each of broad bean cultivar Aquadulce and all parasites attached to them from two fields. At each harvest, we randomly picked four plants each from two treatments (more than five and less than five parasites per host) to examine intraspecific competition. We checked broomrape for phytoparasitic fungi and seed-predating insects, and no infection was found. We counted stems and capsules, oven-dried all organs at 75°C until constant weight was reached, and weighed them. After determining total dry weight, seed dry weight, and seed number of 100 randomly chosen capsules, we spread the capsule seed loads on transparencies, scanned them with a commercial scanner (Hewlett-Packard Scanjet 5400c), and counted the seeds with an image-processing public domain software (Scion Image, www.scioncorp.com; verified 19 July 2005). Regression analysis was done with SigmaPlot (version 6.00, SPSS Inc., Chicago, IL) statistical software.

Model Description
Input variables to the seedbank model include soil moisture (0- to 15- and 15- to 30-cm depth), precipitation, broad bean root length density (0- to 15-cm depth), and the dry weight of emerged parasites, all of which are computed daily by the respective APSIM modules (Fig. 1) . The model outputs numbers of viable seeds for each layer. In compliance with the general APSIM approach, the seedbank model is event based: events occurring in the course of crop development evoke changes of the seedbank. Simulations start at an initial seedbank of S_i viable crenate broomrape seeds per unit area, vertically organized in three layers: soil surface, 0- to 15-, and 15- to 30-cm depth. Seeds on the surface cannot have contact with root exudates while no germination is expected in seeds buried more than 15 cm deep due to dormancy. Therefore, only seeds in 0- to 15-cm depth have the potential to germinate. Simulations are performed for single points in space; hence, homogeneous horizontal seed distribution has to be assumed.

View larger version (38K): [in this window] [in a new window]   Fig. 1. Flow diagram of the seedbank model. Dashed arrows represent input of information from APSIM modules (module names printed in bold). Simulated crop phenology and rules defined in the APSIM-Manager module determine the timing of processes. The initial seedbank is specified by user. For detailed information on the APSIM framework, see Keating et al. (2003).

[1]

Broomrape seeds can attach to roots from up to a 3-mm distance (Linke, 1987). Based on findings of Ruggiero et al. (1999), and considering that the top soil layer mostly contains primary roots, we assume a mean root radius of 0.6 mm. Seed stimulation can occur within a radius of 3.6 mm around the root center. The probability of seed stimulation equals the proportion of soil volume permeated by exudates. Assuming homogeneous root distribution without overlaying, the probability of seed stimulation (p_s) is:

[2]

where R_max is maximum broad bean root length density in 0- to 15-cm depth (in mm mm^–3) and values of p_s range from 0 to 1. Predicted stimulation probability approaches 1.0 at R_max of 0.024 mm mm^–3, which agrees well with field data (Manschadi et al., 2001; Grenz et al., 2005). Even under optimal conditions, 38% of stimulated viable seeds were found not to germinate (Schnell et al., 1996). In addition, prolonged imbibition can induce secondary dormancy (Kebreab and Murdoch, 1999a), which we assume to affect 33% of the seedlot. Therefore, the number of germinated seeds (S_g) removed from the seedbank is:

[3]

Numbers of attached and emerged parasites are calculated in APSIM-Parasite based on equations derived by Manschadi et al. (2001). The number of emerged parasites minus S_g equals the number of ineffectively germinated seeds.

Reproduction
From observations made during the field trials in Adana (Grenz et al., 2005), we infer that broomrape seed production starts 200°Cd after emergence. In the collected specimens, capsule number per plant varied from 11 to 133 and was linearly proportional to parasite dry weight (Fig. 2) . Mean capsule dry weight was 34.9 mg [standard error (SE): 1.63 mg]. Capsules contained an average 3393.5 seeds (153.10), which agrees with data of López-Granados and García-Torres (1991) and Schnell (1993). Mean seed weight was 4.2 µg (0.19). Relations between all parameters were stable. Figure 3 illustrates crenate broomrape reproduction. Based on these findings, seed number in the model is calculated from the biomass of emerged parasites (W_e). Capsule number (C) is proportional to W_e (Fig. 2):

[4]

View larger version (15K): [in this window] [in a new window]   Fig. 2. Linear regression of mean capsule number versus dry weight of crenate broomrape collected from field trials in Adana (Turkey) in 2001 and 2002.

View larger version (41K): [in this window] [in a new window]   Fig. 3. Quantities in crenate broomrape reproduction. Means of specimens collected from field trials in Adana (Turkey) in 2001 and 2002.

[5]

The model simulates the gradual release of seeds from capsules successively splitting open by adding 5% of the produced seeds to the surface seed class daily, starting 400°Cd after parasite emergence. At harvest, all seeds remaining on the parasite are moved to the soil surface.

Viability Loss
Seed survival in the soil is mainly controlled by temperature and humidity and follows negative exponential distributions (Baskin and Baskin, 2001). Schnell (1993) found the decay of crenate broomrape seeds to progress faster in moist soil. The time course of seed viability in the model is described by a negative exponential function whose slope is determined by soil moisture. Soil layers are considered wet or dry, depending on whether moisture level is above or below a threshold level, which was set at 60% plant-available water capacity. In the surface layer, all days with rainfall are considered wet. Between events, wet (D_w) and dry (D_d) days are accumulated. On dry days, 0.05% of seeds lose viability while on wet days, 0.25% die. Equations for wet [6] and dry [7] conditions read

[6]

[7]

where S_t represents the actual seed lot and L_d and L_w represent the number of crenate broomrape seeds losing viability on dry and wet days, respectively. Viability loss is calculated at sowing, host emergence, host flowering, harvest, and tillage operations by layerwise subtracting L_w and L_d from S_t and resetting both variables to zero.

Seed Redistribution
APSIM simulates tillage operations as defined by timing and implement (Probert et al., 1998). Algorithms for vertical seed redistribution were parameterized for no-till, chisel plow, and moldboard plow using data of Staricka et al. (1990). Chisel plowing incorporates seeds to 15-cm depth, leaving 33% at the surface and burying 67% 0 to 15 cm deep. Moldboard plowing leaves 20% of seeds on the surface and buries 40% 0 to 15 cm deep and 40% 15 to 30 cm deep. If no tillage is applied, 3% of seeds from each layer move to the layer below to account for processes other than tillage.

Control Measures
Control methods are classified according to whether they (i) reduce parasite capsule or seed number, (ii) eliminate buried parasite seeds, or (iii) reduce parasite biomass by removing all parasites or emerged parasites only. Approaches applying these principles include (i) biological control by insects or fungi, (ii) physical control by soil solarization, and (iii) manual control by hand-pulling or chemical control by herbicides. These and further approaches can by simulated using the APSIM-Manager module, which applies fixed schedules or conditional logic for timing (McCown et al., 1996).

Model Application
Sensitivity Analysis
We tested the capability of the model to predict seedbank decay by running 10-yr simulations driven by historical weather records from two locations. Tel Hadya (36°0' N, 37°0' E) has a semiarid Mediterranean–continental transition climate while Adana (37°0' N 35°24' E) has a humid Mediterranean climate (Fig. 4) . Virtual fields were cropped with nonhosts every winter and left fallow in the summer. Model predictions were evaluated against published data.

View larger version (21K): [in this window] [in a new window]   Fig. 4. Average monthly rainfall (historical records, columns) and soil moisture content (predicted by APSIM-SoilWat2, line) in the top 15 cm of soil at Tel Hadya and Adana. dul = drained upper limit, ll_15 = lower limit of plant-extractable soil moisture.

Tillage
Effects of tillage intensity on the seedbank were explored. The experimental location was Adana, and agronomy and initial infestation were similar to the previous simulations. Broad bean was grown every fifth season during 25 yr. One of three tillage treatments was applied after harvest of each crop: no-till, shallow cultivation with a chisel plow, or deep cultivation with a moldboard plow.

Control
We also tested the efficacy of hand-pulling schedules. Broad bean was sown at Adana every fifth season. Initial infestation in 0- to 15-cm depth was either 1000 seeds m^–2 or 30000 seeds m^–2. For crop husbandry, see Table 1. Hand-pulling was simulated by removing the biomass of emerged parasites and allowing for regrowth. This method is very labor intensive, and workers often miss some shoots or let them fall to the ground. We therefore assumed a maximum weeding efficacy of 95%, which decreases with increasing parasite biomass. Hand-pulling was scheduled (i) on a fixed date, (ii) a defined number of days after broad bean flowering or broomrape emergence, and (iii) as soon as broomrape dry weight exceeded a threshold value. The optimum sowing window for parasite-infected crops grown at Adana was determined in a previous study where seven sowing dates from 22 October until 22 January were tested, with the latter yielding the best results (Grenz, 2004). To assess the efficacy of combined control strategies, this sowing date was stepwise combined with the best tillage option and the most effective hand-pulling schedule. Initial infestation was 1000 seeds m^–2, and broad bean was sown every third season. The aim was to maximize broad bean yield in infested fields.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Model Application
Sensitivity Analysis
Depending on moisture regime, the model predicted annual viability losses of 33 to 48% (Fig. 5) . This is in accordance with annual viability losses of 42 to 50% reported for buried crenate broomrape seeds from Spain (Cubero and Moreno, 1979; López-Granádos and García-Torres, 1997). In field trials conducted at Tel Hadya, Schnell (1993) found viability losses of 8% under fallow during summer and 25% under broad bean during winter. Model predictions for the same periods and conditions were 9 and 23%, respectively.

View larger version (19K): [in this window] [in a new window]   Fig. 5. Model-predicted viability loss of buried crenate broomrape seeds under semiarid (Tel Hadya) and humid (Adana) Mediterranean conditions.

View larger version (17K): [in this window] [in a new window]   Fig. 6. Simulated broad bean pod yield and crenate broomrape seedbank as affected by environment. Black columns = yield of noninfected broad bean, white columns = yield of infected broad bean, and line = seedbank in 0- to 15-cm depth. The position of the black column indicates the year of broad bean harvest.

View larger version (39K): [in this window] [in a new window]   Fig. 7. Simulated broad bean pod yield and crenate broomrape seedbank as affected by broad bean cropping intensity. Symbol meanings as in Fig. 6.

View larger version (20K): [in this window] [in a new window]   Fig. 8. Simulated broad bean pod yield and crenate broomrape seedbank as affected by tillage system. Symbol meanings as in Fig. 6.

View larger version (45K): [in this window] [in a new window]   Fig. 9. Simulated pod yield [means (±1 SE) of nine cropping seasons] of broad bean infected with crenate broomrape as affected by combinations of sowing date, tillage, and hand-pulling. Symbol meanings: x = sowing delayed from 15 November to 22 January, no-till instead of moldboard plow, hand-pulling 21 d after host flowering or 20 and 30 d after host flowering, respectively.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

Overall, the combined seedbank and competition model has proven a potentially valuable tool for assessments of short- and long-term consequences of parasitic weed infestation and of strategies to tackle this problem. The APSIM framework is an appropriate environment for the simulation of weed seedbank dynamics. The flexibility offered by the APSIM-Manager module, the soil-centered approach of the framework, and the possibility to run multi-season simulations are useful features. Model predictions corroborate the hypothesis that crenate broomrape can only be managed by a combined strategy. When solely applied, neither crop rotation nor reduced tillage nor hand-pulling could contain the parasite seedbank. Combinations of optimized sowing date, stage-dependent hand-pulling, and no-till seem capable of providing long term stability of yields at a profitable level. Adding further approaches, such as trap cropping or biological control, may facilitate rotations with practicable broad bean cropping intensities.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Baskin, C.C., and J.M. Baskin. 2001. Seeds. Ecology, biogeography and evolution of dormancy and germination. Academic Press, London.
• Cubero, J.I., and M.T. Moreno. 1979. Agronomic control and sources of resistance in Vicia faba to Orobanche sp. p. 41–80. In I.A. Bond et al. (ed.) Some current research on Vicia faba in Western Europe. Commission of the European Communities, Luxemburg.
• Foy, C.F., J. Rakesh, and R. Jacobsohn. 1989. Recent approaches for chemical control of broomrape (Orobanche spp.). Rev. Weed Sci. 4:123–152.
• Ghersa, C.M., and M.A. Martínez-Ghersa. 2000. Ecological correlates of weed seed size and persistence in the soil under different tilling systems: Implications for weed management. Field Crops Res. 67:141–148.
• González-Andujár, J.L., A. Martínez-Cob, F. López-Granados, and L. García-Torres. 2001. Spatial distribution of crenate broomrape (Orobanche crenata) infestation in continuous broad bean (Vicia faba) cropping for six years. Weed Sci. 49:414–422.
• Grenz, J. 2004. Using a simulation model to evaluate parasitic weed management strategies: Case study of the association Vicia faba–Orobanche crenata. Agroecology 7. APIA-Verlag, Laubach, Germany.
• Grenz, J., A.M. Manschadi, F.N. Uygur, and J. Sauerborn. 2005. Effects of environment and sowing date on the assimilate competition between broad bean (Vicia faba) and the parasitic weed Orobanche crenata. Field Crops Res. 93:300–313.
• ICARDA. 1989. Food legume improvement program. Annu. Rep. for 1989. ICARDA, Aleppo, Syria.
• Jurado-Expósito, M., L. García-Torres, and M. Castejon-Muñoz. 1997. Broad bean and lentil seed treatments with imidazolinones for the control of broomrape (Orobanche crenata). J. Agric. Sci. (Cambridge) 129:307–314.
• Keating, B.A., P.S. Carberry, G.L. Hammer et al. 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18:267–288.[CrossRef]
• Keatinge, J.D.H. 1985. The influence of precipitation regime on the management of three-course crop rotations in northern Syria. J. Agric. Sci. 104:281–287.
• Kebreab, E., and A.J. Murdoch. 1999a. A quantitative model for loss of primary dormancy and induction of secondary dormancy in imbibed seeds of Orobanche spp. J. Exp. Bot. 50:211–219.[Abstract/Free Full Text]
• Kebreab, E., and A.J. Murdoch. 1999b. A model of the effects of a wide range of constant and alternating temperatures on seed germination of four Orobanche species. Ann. Bot. (London) 84:549–557.[Abstract/Free Full Text]
• Kebreab, E., and A.J. Murdoch. 2001. Simulation of integrated control strategies for Orobanche spp. based on a life-cycle model. Exp. Agric. 37:37–51.
• Kropff, M.J., and L.A.P. Lotz. 1992. Systems approaches to quantify crop–weed interactions and their application in weed management. Agric. Syst. 40:265–282.
• Kropff, M.J., P.S. Teng, and R. Rabbinge. 1995. The challenge of linking pest and crop models. Agric. Syst. 49:413–434.
• Kukula, S.T., and H. Masri. 1984. Integrated cultural practices and chemical control of Orobanche crenata in broad bean. p. 256–261. In Proc. Int. Symp. on Parasitic Weeds, 3rd, Aleppo, Syria. 7–9 May 1984. ICARDA, Aleppo, Syria.
• Linke, K.H. 1987. Untersuchungen über Keimung und Jugendentwicklung von Striga und Orobanche. (In German.) PLITS 1987/5(3). Verlag Josef Margraf, Weikersheim, Germany.
• Linke, K.H. 1992. Biology and control of Orobanche in legume crops. PLITS 1992/10(2). Verlag Josef Margraf, Weikersheim, Germany.
• Linke, K.H., and M.C. Saxena. 1991. Towards an integrated control of Orobanche spp. in some legume crops. p. 248–256. In K. Wegmann and L.J. Musselman (ed.) Progress in Orobanche research. Univ. of Tübingen, Tübingen, Germany.
• López-Granádos, F., and L. García-Torres. 1991. Estimation of Orobanche crenata seed production. p. 92–98. In K. Wegmann and L.J. Musselman (ed.) Progress in Orobanche research. University of Tübingen, Tübingen, Germany.
• López-Granádos, F., and L. García-Torres. 1993. Seedbank and other demographic parameters of broomrape (Orobanche crenata Forsk.) populations in broad bean (Vicia faba L.). Weed Res. 33:319–327.
• López-Granádos, F., and L. García-Torres. 1996. Effects of environmental factors on dormancy and germination of crenate broomrape (Orobanche crenata). Weed Sci. 44:284–289.
• López-Granádos, F., and L. García-Torres. 1997. Modelling the demography of crenate broomrape (Orobanche crenata) as affected by broad bean (Vicia faba) cropping frequency and planting date. Weed Sci. 45:261–268.
• Manschadi, A.M., J.N.G. Hargreaves, J. Grenz, P. DeVoil, and H. Meinke. 2004. Simulating damage effects of parasitic weeds in APSIM: A generic cohort-based approach [Online]. Available at www.cropscience.org.au/icsc2004/poster/2/8/1218_manschadi.htm (verified 29 July 2005). Proc. Int. Crop Sci. Conf., 4th, Brisbane, Australia. The Regional Institute Ltd., Gosford, Australia.
• Manschadi, A.M., J. Sauerborn, and H. Stützel. 2001. Quantitative aspects of Orobanche crenata infestation in broad beans as affected by abiotic factors and parasite soil seedbank. Weed Res. 41:311–324.
• Manschadi, A.M., J. Sauerborn, H. Stützel, W. Göbel, and M.C. Saxena. 1998. Simulation of broad bean (Vicia faba L.) root system development under Mediterranean conditions. Eur. J. Agron. 9:259–272.
• Manschadi, A.M., E. Wang, M.J. Robertson, H. Meinke, and J. Sauerborn. 2003. Development of a parasite module in APSIM—case study: The parasitic weed Orobanche crenata infesting broad bean [Online]. Available at www.regional.org.au/au/asa/2003/c/18/manschadi.htm (verified 19 July 2005). Proc. Aust. Agron. Conf., 11th, Geelong, Australia. Aust. Soc. of Agron., Horsham, Australia.
• McCown, R.L., G.L. Hammer, J.N.G. Hargreaves, D.P. Holzworth, and D.M. Freebairn. 1996. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst. 50:255–271.[CrossRef][ISI]
• Moeller, C. 2004. Sustainable management of a wheat–chickpea rotation in a Mediterranean environment: Scenario analysis using a cropping systems simulator. Agroecology 6. APIA-Verlag, Laubach, Germany.
• Musselman, L.J. 1980. The biology of Striga, Orobanche, and other root-parasitic weeds. Annu. Rev. Phytopathol. 18:463–489.[CrossRef][ISI]
• Nelson, R. 2003. CLIMGEN—climatic data generator [Online]. Available at www.bsyse.wsu.edu/climgen/ (verified 19 July 2005). Washington State Univ., Pullman.
• Parker, C., and C.R. Riches. 1993. Parasitic weeds of the world: Biology and control. CAB Int., Wallingford, UK.
• Pieterse, A.H., L. García-Torres, O.A. al-Menoufi, K.H. Linke, and S.J. ter Borg. 1994. Integrated control of the parasitic angiosperm Orobanche (broomrape). p. 695–702. In F.J. Muehlbauer and W.J. Kaiser (ed.) Expanding the production and use of cool season food legumes. Kluwer, Dordrecht, the Netherlands.
• Probert, M.E., P.S. Carberry, R.L. McCown, and J.E. Turpin. 1998. Simulation of legume–cereal systems using APSIM. Aust. J. Agric. Res. 49:317–327.
• Ruggiero, C., S. de Pascale, and M. Fagnano. 1999. Plant and soil resistance to water flow in broad bean (Vicia faba L. major Harz.). Plant Soil 210:219–231.
• Sauerborn, J. 1991. Parasitic flowering plants—ecology and management. Verlag Josef Margraf, Weikersheim, Germany.
• Sauerborn, J., K.H. Linke, M.C. Saxena, and W. Koch. 1989. Solarization: A physical control method for weeds and parasitic plants (Orobanche spp.) in Mediterranean agriculture. Weed Res. 29:391–397.
• Schnell, H. 1993. Aspekte zur Kontrolle von Orobanche crenata Forsk. in Nordsyrien durch Fruchtfolgemaßnahmen unter Berücksichtigung der Dynamik der Orobanche-Samenpopulation im Boden. (In German, with English abstract.) PLITS 1993/11(2). Verlag Josef Margraf, Weikersheim, Germany.
• Schnell, H., M. Kunisch, M.C. Saxena, and J. Sauerborn. 1996. Simulation of the seedbank dynamics of Orobanche crenata Forsk. in some crop rotations in northern Syria. Exp. Agric. 32:395–403.
• Staricka, J.A., P.M. Burford, R.R. Allmaras, and W.W. Nelson. 1990. Tracing the vertical distribution of simulated shattered seeds as related to tillage. Agron. J. 82:1131–1134.[Abstract/Free Full Text]
• ter Borg, S.J., and A. van Ast. 1991. Soil moisture, root architecture and broomrape (Orobanche crenata) infestation in broad bean (Vicia faba). p. 278–292. In K. Wegmann and L.J. Musselman (ed.) Progress in Orobanche research. Univ. of Tübingen, Tübingen, Germany.
• Thompson, K. 1987. Seeds and seed banks. New Phytol. 106(suppl.):23–34.
• van Hezewijk, M.J., K.H. Linke, F. López-Granados et al. 1994. Seasonal changes in germination response of buried seeds of Orobanche crenata Forsk. Weed Res. 34:369–376.
• Vyas, B.P. 1966. Tobacco—control of Orobanche. Indian Farming 16:51.

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Grenz, J. H. Articles by Sauerborn, J. Search for Related Content PubMed Articles by Grenz, J. H. Articles by Sauerborn, J. Agricola Articles by Grenz, J. H. Articles by Sauerborn, J. Related Collections Weed Management Other Legumes Pest Management Systems Crop Models