A theoretical framework for biological control of soil-borne plant pathogens: Identifying effective strategies

Ecological Dynamics and Microbial Treatments against Oomycete Plant Pathogens

In this review, we explore how ecological concepts may help assist with applying microbial biocontrol agents to oomycete pathogens. Oomycetes cause a variety of agricultural diseases, including potato late blight, apple replant diseases, and downy mildew of grapevine, which also can lead to significant economic damage in their respective crops. The use of microbial biocontrol agents is increasingly gaining interest due to pressure from governments and society to reduce chemical plant protection products. The success of a biocontrol agent is dependent on many ecological processes, including the establishment on the host, persistence in the environment, and expression of traits that may be dependent on the microbiome. This review examines recent literature and trends in research that incorporate ecological aspects, especially microbiome, host, and environmental interactions, into biological control development and applications. We explore ecological factors that may influence microbial biocontrol agents’ efficacy and discuss key research avenues forward.

Biopolymers for Biological Control of Plant Pathogens: Advances in Microencapsulation of Beneficial Microorganisms

The use of biofertilizers, including biocontrol agents such as Pseudomonas and Bacillus in agriculture can increase soil characteristics and plant acquisition of nutrients and enhancement the efficiency of manure and mineral fertilizer. Despite the problems that liquid and solid formulations have in maintaining the viability of microbial agents, encapsulation can improve their application with extended shelf-life, and controlled release from formulations. Research into novel formulation methods especially encapsulation techniques has increased in recent years due to the mounting demand for microbial biological control. The application of polymeric materials in agriculture has developed recently as a replacement for traditional materials and considered an improvement in technological processes in the growing of crops. This study aims to overview of types of biopolymers and methods used for encapsulation of living biological control agents, especially microbial organisms.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Dispersal of Bacillus subtilis and its effect on strawberry phyllosphere microbiota under open field and protection conditions

Using biological control agents (BCAs) is an essential component of integrated pest and diseases management. Despite much research on biocontrol of plant diseases, success in field crops has been limited with most successes being achieved in greenhouse cultivation. This lack of success is often attributed to the complex ecological processes involved in biocontrol. We used next generation sequencing (NGS) technology to study environmental fate of Bacillus subtilis, a widely used BCA, focusing on its dispersal aspect in open field and under protection. The dispersal of B. subtilis was very limited, particularly under protection. The reduction in the BCA population size was relatively small within 8 days; indeed, no overall reduction in the relative abundance was observed under the protected condition. These results suggested that limited dispersal is probably the main reason for its variable (and often low) control efficacy under field conditions. Thus to increase biocontrol efficacy, it is necessary to frequently apply this BCA with the application interval depending on the growth rate of target host tissues. Phyllosphere microbiota differed significantly between plants grown in open field and under protection but were not greatly affected by the introduced BCA.

The effect of time delay in plant--pathogen interactions with host demography

Botanical epidemic models are very important tools to study invasion, persistence and control of diseases. It is well known that limitations arise from considering constant infection rates. We replace this hypothesis in the framework of delay differential equations by proposing a delayed epidemic model for plant--pathogen interactions with host demography. Sufficient conditions for the global stability of the pathogen-free equilibrium and the permanence of the system are among the results obtained through qualitative analysis. We also show that the delay can cause stability switches of the coexistence equilibrium. In the undelayed case, we prove that the onset of oscillations may occur through Hopf bifurcation.

Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus

A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify-the optimal host spacing and the optimal roguing schedule-are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood.

Theoretical modeling suggests that synergy may result from combined use of two biocontrol agents for controlling foliar pathogens under spatial heterogeneous conditions

There has been a trend for combined use of several biocontrol agents (BCAs) with an expectation of synergistic interactions among BCAs. However, previous modeling studies suggested that, under homogeneous and temporal-fluctuating conditions, combined use of two BCAs, in most cases, only results in efficacies similar to the more efficacious one used alone; a result consistent with published experimental data. The present modeling study investigated whether combined use of two mycoparasitic BCAs, two competitive BCAs, or a mycoparasitic and a competitive BCA leads to synergistic interactions under spatially heterogeneous conditions. In the model, there were two patches with varying relative sizes and two BCAs differentially adapted to the two patches. Within the range of model parameter values considered, combined use of two BCAs is more effective than the more efficacious BCA used alone in 72% of the simulated cases. There was also a considerable proportion (≈21%) of model simulations in which combined use of two BCAs led to synergy (i.e., efficacy was greater than expected under the assumption of Bliss independence, especially when each of the two BCAs can only survive in one [different] patch). Combined use of a mycoparasitic BCA with a competitive one is more likely to result in synergy than the other two BCA combinations. When biocontrol activities of individual BCAs are low or moderate, biocontrol efficacy arising from combined use of two BCAs does not depend greatly on biocontrol mechanisms. However, for high BCA activities, combined use with at least one competitive BCA resulted in better control than combined use of two mycoparasitic BCAs. The present modeling study emphasized the need for understanding the degree of spatial patchiness and quantitative relationships between biocontrol activities and external conditions in order to apply commercial BCAs effectively.

Combined use of two biocontrol agents with different biocontrol mechanisms most likely results in less than expected efficacy in controlling foliar pathogens under fluctuating conditions: a modeling study

Effective use of biocontrol agents (BCAs) is a potentially important component of sustainable agriculture; recently, there has been a trend for combined use of several BCAs, with an expectation of synergistic interactions among them. A previous numerical study suggested that, under homogenous conditions in which two BCAs occupied the same host tissue as the pathogen, combined use of two BCAs with different biocontrol mechanisms resulted, in most cases, in efficacies similar to using the more efficacious one alone; this result is consistent with published experimental results. The present study investigates whether combined use of a mycoparasitic and a competitive BCA leads to greater efficacy than that expected when the model is modified to allow for fluctuating temperature regimes and the effects of temperature on the pathogen and BCAs. Within the range of parameter values considered, combined use of two BCAs is shown to be less effective than that expected under the assumption of Bliss independence, and to result in a level of efficacy similar to that achieved by the more efficacious component used alone, indicating antagonistic interactions between the two BCAs. Nevertheless, combined use of two BCAs resulted in a slightly longer delay in epidemic development than did individual use of BCAs. Stochastic variability in simulated hourly temperatures did not result in a high level of variability in efficacy among replicates; nevertheless, the among-replicate variability appeared to be greater for the combined use of BCAs than for individual BCAs used alone. In contrast, there were greater effects of varying BCA-temperature relationships and application time (reflected in the temperature profile) on efficacy, suggesting the importance of characterizing the relationship between BCA activity and environmental conditions in future research.

Pathogen variation and urea influence selection and success of Streptomyces mixtures in biological control

Success in biological control of plant diseases remains inconsistent in the field. A collection of well-characterized Streptomyces antagonists (n = 19 isolates) was tested for their capacities to inhibit pathogenic Streptomyces scabies (n = 15 isolates). There was significant variation among antagonists in ability to inhibit pathogen isolates and among pathogens in their susceptibility to inhibition. Only one antagonist could inhibit all pathogens, and antagonist-pathogen interactions were highly specific, highlighting the limitations of single-strain inoculum in biological control. However, the collection of pathogens could be inhibited by several combinations of antagonists, suggesting the potential for successful antagonist mixtures. Urea generally increased effectiveness of antagonists at inhibiting pathogens in vitro (increased mean inhibition zones) but its specific effects varied among antagonist-pathogen combinations. In greenhouse trials, urea enhanced the effectiveness of antagonist mixtures relative to individual antagonists in controlling potato scab. Although antagonist mixtures were frequently antagonistic in the absence of urea, all n= 2 and n = 3 antagonist-isolate combinations were synergistic in the presence of urea. This work provides insights into the efficacy of single- versus multiple-strain inocula in biological control and on the potential for nutrients to influence mixture success.

Time-dependent infectivity and flexible latent and infectious periods in compartmental models of plant disease

Compartmental models have become the dominant theoretical paradigm in mechanistic modeling of plant disease and offer well-known advantages in terms of analytic tractability, ease of simulation, and extensibility. However, underlying assumptions of constant rates of infection and of exponentially distributed latent and infectious periods are difficult to justify. Although alternative approaches, including van der Plank's seminal discrete time model and models based on the integro-differential formulation of Kermack and McKendrick's model, have been suggested for plant disease and relax these unrealistic assumptions, they are challenging to implement and to analyze. Here, we propose an extension to the susceptible, exposed, infected, and removed (SEIR) compartmental model, splitting the latent and infection compartments and thereby allowing time-varying infection rates and more realistic distributions of latent and infectious periods to be represented. Although the model is, in fact, more general, we specifically target plant disease by demonstrating how it can represent both the van der Plank model and the most commonly used variant of the Kermack and McKendrick (K & M) model (in which the infectivity response is delay Gamma distributed). We show how our reformulation retains the numeric and analytic tractability of SEIR models, and how it can be used to replicate earlier analyses of the van der Plank and K & M models. Our reformulation has the advantage of using elementary mathematical techniques, making implementation easier for the nonspecialist. We show a practical implication of these results for disease control. By taking advantage of the easy extensibility characteristic of compartmental models, we also investigate the effects of including additional biological realism. As an example, we show how the more realistic infection responses we consider interact with host demography and lead to divergent invasion thresholds when compared with the "standard" SEIR model. An ever-increasing number of analyses purportedly extract more biologically realistic invasion thresholds by adding additional biological detail to the SEIR model framework; we contend that our results demonstrate that extending a model that has such a simplistic representation of the infection dynamics may not, in fact, lead to more accurate results. Therefore, we suggest that modelers should carefully consider the underlying assumptions of the simplest compartmental models in their future work.