© John Libbey Eurotext 2011

Introduction

Because of environmental and health safety issues, European and national legislators have called for a drastic decrease in herbicide applications. As a consequence, weeds are expected to increase and diversify in crops, together with a series of companion bioagressors among which parasitic weeds from the Orobanchaceae family. In France, Phelipanche ramosa (L.) Pomel (Joel, 2009) is the most frequent and harmful species (Benharrat et al., 2005; Gibot-Leclerc et al., 2006). It has been reported in 27 departments and can cause up to 80% of yield loss in its favourite crop, i.e. oilseed rape (Gibot-Leclerc et al., 2009). Phelipanche ramosa can infect a wide range of crops (tobacco, hemp, melon…) as well as weeds (Gibot-Leclerc et al., 2009; Gibot-Leclerc et al., 2003; Boulet et al., 2007) and is thus expect to proliferate in case of less stringent weed control.

At present, there are no commercial herbicides available against P. ramosa and curative measures are limited to manual weeding (Rubiales et al., 2009). The control of the parasite is mostly based on preventive methods such as tillage to limit the contact of host roots and parasite seeds (Rubiales et al., 2009), growing false hosts (which stimulate parasite germination but are immune to fixation) and trap plants (which stimulate germination and are destroyed before parasite seed production) to stimulate fatal parasite germination (Lins et al., 2006), or decreasing the frequency of susceptible crops and varieties in the rotation (Buschmann et al., 2005). Consequently, cultivation techniques must be judiciously chosen and combined to optimize cropping systems to control both the parasite and susceptible non-parasitic weeds.

It is now well recognized that models that quantify the effects of cropping systems on pest dynamics are valuable tools to synthesize knowledge on bioagressor life-cycle and design management strategies (Aubertot et al., in press; Rossing et al., 1997; Colbach, 2010). To understand and predict the variability in effects observed for given techniques and to use these models in a large range of conditions without reparametrising, mechanistic approaches where life-cycles are split into sub-processes depending on biological and physical effects of cropping systems, in interaction with the biological (e.g. weed stage) and physical conditions (e.g. soil structure) are necessary (Colbach and Debaeke, 1998; Colbach et al., 2005). Consequently, the objective of the present paper was to analyze existing weed dynamics models and literature data on parasitic weeds to develop a preliminary model of cropping system effects on the dynamics of P. ramosa, in interaction with non-parasitic weed flora.

Material and methods

Model organization

The analysis of existing weed dynamics models in literature (see reviews by (Colbach, 2010; Colbach and Debaeke, 1998; Colbach et al., 2005; Doyle, 1997; Holst et al., 2007)) showed that to date, only two models answer our requirements for a mechanistic model based on biophysical sub-models predicting processes as a function of cropping systems and environmental conditions. These are the monospecific prototype AlomySys (Colbach et al., 2006a; Colbach et al., 2006b; Colbach et al., 2007) developed for an annual grass weed (i.e. Alopecurus myosuroides Huds.) and its multi-specific successor FlorSys (Colbach et al., 2008a; Gardarin, 2008; Colbach et al., 2010a). The life-cycle of P. ramosa has not yet been modelled but another species of Orobanchaceae, Orobanche crenata, has already been tackled (Grenz et al., 2005).

In the present work, we developed a new model called PheraSys for the effects of cropping systems on the dynamics of Phelipanche ramosa. The structure of the new model was inspired by that of FlorSys for those processes that parasitic and non-parasitic weeds have in common (e.g. seed movements during tillage); specific parasitic stages were described according to the O. crenata model. The input variables of FlorSys and PheraSys consist of:

• the above-ground climate: temperature and rainfall for each simulated day;

• a description of the simulated location: soil texture and depth, initial soil structure (fragmented, intermediate, compacted) and initial soil moisture (dry, intermediate, moist);

• the initial weed seed bank: seed density for each weed species, soil layer (30 soil layers ranging from 0 to 30 cm) and seed age class (freshly produced vs. older than one year);

• the cropping system during the whole simulated period, comprising the crop sequence including set-aside and cover crops, the date of all operations (tillage, sowing, herbicides, mechanical weeding, mowing, nitrogen fertilization, manure, harvest) and their characteristics, i.e. tool, working depth etc. for tillage, active ingredient, rate and conditions (good, intermediate, bad) for herbicides, seed density and row spacing for sowing, and total applied rate for nitrogen fertilization.

In addition, PheraSys uses several intermediate and output variables produced by FlorSys (figure 1):

• the proportions of seeds moved between soil layers during tillage, depending on tillage characteristics and soil structure;

• soil hydro-thermal conditions: temperature, soil moisture and water potential for each day and soil layer are predicted with a submodel extracted from STICS (Brisson et al., 1998);

• the density of crop and weed plants as well as their root lengths in each soil layer.

Figure 1. Input and output variables of the newly developed PheraSys model predicting the dynamics of the parasitic weed Phelipanche ramosa, in interaction with soil environment and weed variables predicted by the existing FlorSys model developed for non-parasitic weeds (Colbach et al., 2008a; Gardarin, 2008; Colbach et al., 2010a).

These input variables influence the annual life-cycle of non-parasitic weeds in FlorSys (Colbach et al., 2008a; Gardarin, 2008; Colbach et al., 2010a) and of the parasite in PheraSys.

Model equations and parameters of PheraSys

Seed movements during tillage

Tillage moves soil and seeds, and the degree of burial depends on the tillage implement. For instance, mouldboard ploughing buries more fresh seeds than tilling with a chisel or a covercrop. In PheraSys, a matrix equation is used to transfer P. ramosa seeds between soil layers (table 1, Eq. [1]). The seed movement matrix is estimated by FlorSys as a function of tillage and soil structure.

Seed mortality

Daily in situ seed mortality (Eq. [3]) includes any seed disappearance caused by seed age, diseases or microscopic predators, but excludes any seed bank decrease due to germination which is calculated separately. Surface predation due to birds, rodents or beetles etc. was neglected. Seed mortality was higher in moist vs. dry conditions (Eq. [2]). As no data was available for P. ramosa, the parameter values from O. crenata were used (Grenz et al., 2005).

Pre-conditioning of seeds

Pre-conditioning, i.e. exposure to certain temperature and moisture conditions, is necessary to make seeds susceptible to host root exsudates (Eq. [4] to [6]). It takes three weeks (Gibot-Leclerc et al., 2004) at a temperature between 10 and 25 °C (Gibot-Leclerc et al., 2004; Goldwasser and Yoder, 2001; Zehhar et al., 2002) and a soil water potential between - 2 and - 0.02 MPa (Gibot-Leclerc et al., 2004) to increase the rate of pre-conditioned seeds from 0% at seed maturity to 100%. After 100 days in pre-conditioning conditions, the rate of pre-conditioned seeds drops again to zero (Gibot-Leclerc et al., 2004).

Seed stimulation and germination

Seeds must be located at less than 4 mm from a exsudating host root to be stimulated by host root exsudates and become able to germinate (Gibot-Leclerc, 2004). PheraSys thus calculates the volume of soil in each soil layer that is located close enough to host roots, as a function of host plant density and cumulated root lengths in each soil layer (Eq. [7]). The latter two variables concern both crop and non-parasitic weed species and are predicted by FlorSys though the prediction is at present very simplistic. The soil volume reached by root exsudates is then divided by the total soil layer volume to obtain the proportion of stimulated and germinated parasite seeds. The germinated seeds are calculated once, when the crop no longer produces root exsudates stimulating parasite seeds (e.g. 1006 °C days in oilseed rape, (Gibot-Leclerc, 2004)), and retracted from the surviving parasite seed bank (Eq. [8]).

Parasite fixation and plant emergence

All germinated parasite seeds are assumed to infect a host root (Eq. [9]) as parasite temperature and moisture requirements are usually met after crop sowing (Gibot-Leclerc et al., 2004). However, only a fraction of the fixated parasites succeeds in producing an emerged plant. PheraSys assumes that each host plant can fix a maximum of δ _h emerged parasite plants, with δ _h depending on the host species (Eq. [11]). For instance, no more than 20 parasite plants can fix and emerge per oilseed rape host plant (Gibot-Leclerc et al., 2006). Below this maximum, a density-dependent relationship is used where the probability of emergence of a fixed parasite decreases with increasing number of fixations per host plant (Eq. [10]). Emergence probability does not depend on seed depth (as in the case of non-parasitic weeds) because, thanks to its host plant, the parasite presents a shoot that is sufficiently large not to be hindered by soil clods (Gardarin et al., 2010).

Seed production

At present, no destructive operations are available in crops, so all emerged parasite plants reach maturity. Parasite maturity coincides with crop maturity, regardless of the crop species (Gibot-Leclerc, 2004). Seed production is therefore calculated at crop harvest, with the number of seeds increasing with the parasite biomass (Eq. [13]). As no data was available for P. ramosa, the relevant parameter value for O. crenata was used (Grenz et al., 2005). The latter increases with the growth duration since parasite emergence (approximately since crop sowing, Eq. [12]) and is therefore larger when infesting oilseed rape than tobacco (Buschmann et al., 2005). The newly produced seeds are added to the top layer of the soil seed bank (Eq. [14]).

Simulations

The reference cropping system was a winter oilseed rape (OSR)/winter wheat (WW)/winter barley (WB) rotation, identified as the most common rotation in farm surveys carried out in Côte d’Or, Burgundy (Eastern France) (Colbach et al., 2008b). Table 2 shows the crop management used for the different crops of the rotation. In addition to the reference system, five alternative scenarios were tested ( table 3 ). The six cropping systems were simulated first with PheraSys without any non-parasitic weeds; a second run simulated the dynamics of P. ramosa in the presence of a weed density typical of the tested systems ( table 3 ). All simulations started with 1000 P. ramosa seeds in the top soil layer and lasted for 27 years. When simulating the reference system, weather scenarios were chosen randomly each year among available data sets from Dijon from 1986 to 2004. The same list of weather scenarios was then used for the remaining cropping system simulations.

Results

Figure 2 shows the dynamics of the parasite soil seed bank with time in the reference OSR/WW/WB cropping system (hence R system). Soil seed bank gradually increased over time with enormous increases after the susceptible crop OSR and smaller reductions during the non-susceptible cereal crops. After three repetitions of the rotation (i.e. 9 years), the soil seed bank more or less stabilized around a sill value. Reducing the frequency of susceptible crops in the rotation by adding a spring pea after barley (hence DR system) delays the advent of the sill by three years. Even then, the infestation was still lower after OSR in the DR vs. R simulation. More importantly, the seed bank was considerably lower before sowing the susceptible OSR crop in the DR vs. R system (figure 3). Burying parasite seeds deeply with mouldboard ploughing (MP) did not reduce parasite density. Conversely, the no-till system (NT) was practically parasite-free because there was little chance of contact between the parasite seeds on soil surface and the host roots below. A lower OSR sowing density (LD) slightly decreased parasite seed bank as it resulted in a lower probability of root presence in the parasite vicinity. Delayed sowing (DS) very slightly reduced parasite density because more parasite seeds died before sowing. In all cases, parasite density increased when the field was infested with non-parasitic weeds. The increase was though negligible when non-parasitic weed density was low as in the DR system. Conversely, it was enormous in the NT system where weeds were frequent, resulting in the highest parasite density of all tested systems.

Figure 2. Density of Phelipanche ramosa soil seed bank after crop harvest with time simulated with PheraSys for two different rotations in the absence of non-parasitic weeds.

Figure 3. Density of Phelipanche ramosa soil seed bank before oilseed rape crops in different cropping systems after 12 years of simulation with PheraSys, either with or without non-parasitic weeds. For details of cropping systems and weeds, see table 3.

Discussion

The present work demonstrated that it is possible to develop a partially mechanistic model of cropping systems effects on the dynamics of parasitic weeds. The development of the pre-fixation soil processes largely benefitted from our previous extensive modelling research on non-parasitic weeds though the submodel was parametrized with data from other species (i.e. seed mortality rate of O. crenata). The post-fixation submodel was developed based on results from in vitro experiments (Gibot-Leclerc et al., 2009; Gibot-Leclerc et al., 2003; Gibot-Leclerc et al., 2004) but some stages still remain unclear (e.g. emergence rate of fixations). The post-emergence submodel was rudimentary and entirely based on an existing O. crenata submodel. This first modelling attempt for P. ramosa thus identified major areas on which research should concentrate, among which post-emergence host-parasite relationships and variability in parasite behaviour in different crops.

The present simulations showed the major importance of non-parasitic weeds for the dynamics of the parasite. Though the ability of various weed species to stimulate and fix parasite germinations has already been widely studied (Gibot-Leclerc et al., 2003), the reproductive ability of the parasite on different weeds has rarely been investigated. table 4 though list the most weed species most sensitive to P. ramosa that farmers should well control in their crops if they have P. ramosa problems. Germination-stimulating non-parasitic weeds could be used during the intercrop season to stimulate fatal parasite germinations and thus contribute to reducing the parasite seed bank at crop sowing. This though assumes that pre-sowing tillage and herbicides are sufficiently efficient to destroy all infested weeds.

Among the tested options, zero tillage appeared as particularly interesting for controlling P. ramosa, as long as non-parasitic weeds were rare. This is though only true if parasite seeds remain on soil surface during the whole crop season and do not migrate closer to crop roots due to soil fissures and rain, which is only partially true (Mohler et al., 2006). This also assumes that there are no crop roots sufficiently close to soil surface to stimulate parasite germination. Unfortunately, our knowledge on root growth is much more limited than on above-ground growth and development, and root growth is rarely integrated into cropping system models though it exists in crop models (Keating et al., 2003). This is another interesting future research question.

The simulations also show that once P. ramosa infests the field, it steadily increases until reaching a sill value characteristic of the rotation. This indicates that parasite control must be applied as soon as a field is even only slightly infested. As there are as yet no curative techniques and as other cultural measures are only partially efficient, this mostly means avoiding parasite-susceptible crops in the rotation for several years (Rubiales et al., 2009).

Conclusion

The present work was an exploratory study to evaluate the possibility to develop a mechanistic model of cropping system effects on the dynamics of parasitic weeds, producing a preliminary version of PheraSys. This version not only demonstrated the feasibility of our modelling objective but also identified the major areas on which future research on the parasite should concentrate. The preliminary model also shows how such a model can be used for evaluating existing and prospective cropping system scenarios. Though consistent with existing knowledge on P. ramosa dynamics, these simulation results should be considered as an illustration of the model’s future use. Indeed, they were based on a PheraSys parametrized with data obtained for another parasite species or from expert opinion. Considerable future work is necessary to improve our knowledge on the parasite life-cycle in different crops and management situations.

Acknowledgments

The present work was financed by INRA, ANR OGM VIGIWEED (ANR-07-POGM-003-01) and Regional Action Plan for Innovation (PARI 2010-9201AAO050S01397) of Burgundy region.

References

All Tables

All Figures

	Figure 1. Input and output variables of the newly developed PheraSys model predicting the dynamics of the parasitic weed Phelipanche ramosa, in interaction with soil environment and weed variables predicted by the existing FlorSys model developed for non-parasitic weeds (Colbach et al., 2008a; Gardarin, 2008; Colbach et al., 2010a).
In the text

	Figure 2. Density of Phelipanche ramosa soil seed bank after crop harvest with time simulated with PheraSys for two different rotations in the absence of non-parasitic weeds.
In the text

	Figure 3. Density of Phelipanche ramosa soil seed bank before oilseed rape crops in different cropping systems after 12 years of simulation with PheraSys, either with or without non-parasitic weeds. For details of cropping systems and weeds, see table 3.
In the text