- Top of page
- Materials and Methods
Many arable and, in particular, horticultural systems involve growing crop monocultures in quick succession. The risk of a soil-borne epidemic when crops are grown under such intensive regimes is initially high, and often results in the very rapid build-up of disease from one season to the next (Hide & Read, 1991; Gilligan et al., 1996). Biological control provides an environmentally appealing method of reducing disease yet, even when disease is relatively well controlled in one season, severe disease patches can develop in the next (Schneider et al., 2001). Under field conditions, variation in the severity of epidemics (MacNish, 1996; Schneider et al., 2001) and in the efficiency of biocontrol (Weller, 1988) is commonly observed under a regime of continuous cropping. Much of the variability is undoubtedly caused by environmental heterogeneity in the form of differences in soil type, soil temperature and soil moisture. But underlying this are other stochastic influences, driven by the demographic interactions of the pathogen, root and microbial populations (White & Gilligan, 1998). These demographic factors also determine the course of an epidemic and the outcome of disease control. Consequently, a major challenge in devising a coherent theory for the performance of biological control in botanical epidemics is first, to understand the underlying demographic mechanisms driven by the host–pathogen–biological control complex that together influence the dynamics of disease during successive seasons. Here we initiate this by analysing replicated epidemics in microcosms in which we minimize environmental variation within and between epidemics on consecutive crops.
The production and survival of inoculum in a preceding crop and during the intercropping period is central to the development of disease across seasons (Gilligan et al., 1996; Schneider et al., 2001). The production of inoculum depends on the dynamics of disease in a previous crop. We have shown elsewhere that this depends, in turn, on the balance between primary and secondary infection (Bailey & Gilligan, 1999), as well as changes over time in the susceptibility of the host (Kleczkowski et al., 1996; Gibson et al., 1999). This suggests a simple sequence of events, whereby the amount of initial inoculum determines the levels of primary infection in the first crop. Together with changes in the susceptibility of the host this, in turn, influences the rate of secondary infection and the final level of disease at the end of the first season. Following harvest, diseased tissue is converted into inoculum from which the residual, after decay between crops, initiates the epidemic in the succeeding crop. It follows then that the balance between primary and secondary infection in a first crop and the dynamics of inoculum between crops affects the relative amounts and infectivity of inoculum, and hence the balance between primary and secondary infection in a second crop.
In this paper we ask two broad questions: (1) How do the dynamics of disease differ between successive crops? and (2) How is this influenced by repeated applications of a biocontrol agent? We address these questions using a combination of modelling and experimentation in microcosms filled with sand to provide replicated epidemics of damping-off disease caused by the soil-borne plant pathogen Rhizoctonia solani Kühn on radish (Raphanus sativus L.) in the presence or absence of the biological control agent Trichoderma viride Pers. Ex. Gray. The simplicity of sand omits many of the biological processes, interactions and variability associated with field soils, but improves repeatability and allows us to identify and quantify the main epidemiological features responsible for the spread of disease. For this system we ask the following sequence of questions: (i) What is the balance between primary and secondary infection within the first and second crops? (ii) How does the addition of the biocontrol agent affect disease in the first crop? (iii) Does the presence of the biocontrol agent in the first crop affect disease dynamics in the second crop? (iv) Does this in turn affect the likelihood of successful control in the second crop?
We use the results of these analyses to construct a schematic framework to account for the evolution of epidemiological dynamics across successive crops, and the consequences for biological control.
- Top of page
- Materials and Methods
We have shown how a change in epidemiological dynamics affects the efficiency of biological control of R. solani during consecutive crops. This allows us to explain why control by T. viride may succeed in a first crop but fail in the second, even though the biocontrol agent is reintroduced with each sowing. The change in epidemiological dynamics is caused by differences in the net infectivity of inoculum preceding each crop. This leads to a switch in the balance between primary and secondary infection in the succeeding crop.
In unprotected first crops there was a rapid build-up of disease from a low initial density of inoculum. First crop epidemics were characterized by low levels of primary infection and high levels of secondary infection (Fig. 1). By contrast, second crops were characterized by a significant increase in primary infection, leaving relatively fewer plants available for secondary infection. This switch from secondary to primary infection is consistent with the large increase in net infectivity of inoculum detected before second crops were planted (Table 1).
Application of the biocontrol agent, T. viride, produced an estimated 10-fold reduction in the net infectivity of inoculum (Table 1), as well as a substantial reduction in average levels of disease in first crops. This included six out of 10 replicates for which no damping-off was detected. Where disease was initiated, epidemics were dominated by secondary infection (Fig. 1e) and control of disease was attributed to a reduction in primary infection (Table 1). This is consistent with a previous analysis by Gibson et al. (1999) for biocontrol of R. solani on radish in a single crop, based on fitting a stochastic model for primary and secondary infection. However, when the crop was resown and the control agent reapplied, the efficiency of control was reduced significantly in all replicates, and epidemics were dominated by primary infection (Fig. 1; Table 1). Moreover, in replicates in which disease was completely controlled in first crops, significant outbreaks of disease were detected in second crops, suggesting considerable amplification of inoculum in the absence of disease (damping-off). These results show that inoculum produced from diseased, damped-off plants alone was not the only reservoir of inoculum for infection of a second crop.
Two mechanisms contribute to the amplification of inoculum. The first occurs by parasitic growth during the cropping period and produces inoculum in the form of damped-off plants. That both primary and secondary infection can contribute to the parasitic amplification of inoculum is now evident for a wide range of soil-borne pathogens including, for example, infection of onions by Sclerotium cepivorum (Entwisle, 1990); wheat by Gaeumannomyces graminis (Bailey & Gilligan, 1999; Schoeny & Lucas, 1999); lettuce by Sclerotinia minor (Gubbins & Gilligan, 1997); and tomato by Fusarium oxysporum (Rekah et al., 2001). However, as we have demonstrated here, the precise contribution of primary and secondary infection to final levels of disease depends ultimately on the interaction between initial inoculum density, changes in host susceptibility, and the rates of disease transmission. The second mechanism for amplification of inoculum involves saprotrophic growth during the intercropping period, and produces inoculum in the form of colonized root debris. The competitive, saprotrophic ability of R. solani and other soil-borne plant pathogens has long been recognized (Garrett, 1970), and the contribution of saprotrophic growth to epidemics of plant disease is well documented (Papavizas, 1970), but the consequences for disease control have received little epidemiological analysis. These facultative parasites are capable of extensive saprotrophic multiplication on root residue remaining during the intercropping period, which means that production of inoculum for a second crop is not likely to depend solely on final levels of disease in the first crop. The consequences of these two forms of inoculum production for the spread and biological control of disease in consecutive crops are described schematically in Fig. 2. Disease is initiated by primary infection from particulate inoculum (Fig. 2ai) and spreads by secondary, plant-to-plant infection to create disease patches (Fig. 2aii). During the intercrop period diseased plants are converted to inoculum and, at the same time, the pathogen continues to spread on decaying roots by saprotrophic growth (Fig. 2aiii). This generates large amounts of inoculum (Fig. 2aiv) and high levels of primary infection in the next crop (Fig. 2av). In this study, biocontrol reduced primary infection and disease of the first crop (Fig. 2bii) and the production of inoculum from infected plants, but did not control colonization of roots (Fig. 2biii). This probably reflects the relatively low competitive saprotrophic ability (sensuGarrett, 1970) of Trichoderma vs Rhizoctonia in this system. From colonized roots, large quantities of inoculum developed (Fig. 2iv) to cause high levels of primary infection in a second crop when disease was absent in the first crop (Fig. 2v). It is striking that the inferred amplification of inoculum in the absence of damping-off or other symptoms of disease can result in severe and unexpected outbreaks of disease in the second crop.
Figure 2. A schematic interpretation of the spread of disease and inoculum over consecutive seasons for (a) unprotected and (b) protected radish crops. (i) Inoculum (open circles) initiates disease by primary infection of plants (dots) in the first crop. (ii) Disease in the first crop spreads by secondary infection to create disease patches (dark shading). (iii) After the crop is harvested the pathogen continues to spread by saprotrophic growth. (iv) The combination of disease dynamics and saprotrophic growth determine the density and distribution of inoculum for (v) disease dynamics in a second crop. Biological control may reduce or, as shown here, completely control disease in a first crop, but saprotrophic amplification of inoculum can still compensate for parasitic growth leading to severe and unexpected outbreaks of disease in a second crop.
Download figure to PowerPoint
The analysis of epidemics using mechanistic models offers a powerful but as yet poorly exploited tool with which to study biological control. Moreover, it offers the opportunity to develop a coherent strategy for the long-term management of disease over seasons (Gubbins & Gilligan, 1997) whereby the consequences of control in one season change the dynamics in the following season, necessitating a change in control strategy. It is likely that different forms of control, whether biological, chemical or physical, will affect different components of the parasitic (primary infection and secondary infection) and saprotrophic amplification of inoculum. The modelling and analysis introduced here provide a theoretical and experimental framework to investigate these systems.
While we have proposed a mechanism for the bulking-up of inoculum and disease of R. solani, and for the failure of biological control for crops grown in quick succession, long-term decay in infectivity of inoculum (disease suppression) attributed to microbial competition (Schneider et al., 2001) or the development of native antagonists (Whipps, 1997) is also commonly observed under a regime of continuous cropping (Henis et al., 1978; MacNish, 1988; Weller et al., 2002). The sand system used here was experimentally appealing because of its biological simplicity and homogeneity. A natural progression is to examine the influence of environmental factors such as soil type, soil temperature and soil moisture on the parameters for epidemic development of both average levels of disease and the variability between replicate epidemics. The consequences of such an increase in biological complexity and heterogeneity for the performance of biocontrol in soil await further analysis. The current work shows that epidemiological analysis of disease data can be used to develop a coherent understanding of inoculum dynamics and that, in the absence of this, the performance of biocontrol from one crop to the next will remain unpredictable.