Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction.

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term "endocyclic" refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

Challenges With Managing Disease Complexes During Application of Different Measures Against Foliar Diseases of Field Pea.

Studies were undertaken across five field locations in Western Australia to determine the relative changes in disease severity and subsequent field pea yield from up to four foliar pathogens associated with a field pea foliar disease complex (viz. genera Didymella, Phoma, Peronospora, and Septoria) across four different pea varieties sown at three different times and at three different densities. Delaying sowing of field pea significantly (P < 0.05) reduced the severity of Ascochyta blight (all five locations) and Septoria blight (one location), increased the severity of downy mildew (four locations), but had no effect on seed yield. In relation to Ascochyta blight severity at 80 days after sowing, at all locations the early time of sowing had significantly (P < 0.05) more severe Ascochyta blight than the mid and late times of sowing. Increasing actual plant density from 20 to 25 plants m-2 to 58 to 78 plants m-2 significantly (P < 0.05) increased the severity of the Ascochyta blight (four locations) and downy mildew (one location), and it increased seed yield at four locations irrespective of sowing date and three locations irrespective of variety. Compared with varieties Dundale, Wirrega, and Pennant, variety Alma showed significantly (P < 0.05) less severe Ascochyta blight, downy mildew, and Septoria blight (one location each). Grain yield was highest for the early time of sowing at three locations. Varieties Alma, Dundale, and Wirrega significantly (P < 0.05) outyielded Pennant at four locations. The percentage of isolations of individual Ascochyta blight pathogens at 80 days after the first time of sowing varied greatly, with genus Didymella ranging from 25 to 93% and genus Phoma ranging from 6 to 23% across the five field locations. This fluctuating nature of individual pathogen types and proportions within the Ascochyta blight complex, along with variation in the occurrence of pathogens Peronospora and Septoria, highlights the challenges to understand and manage the complexities of co-occurring different foliar pathogens of field pea. While the search for more effective host resistance continues, there is a need for and opportunities from further exploring and exploiting cultural management approaches focusing on crop sequence diversification, intercropping, manipulating time of sowing and stand density, and application of improved seed sanitation and residue/inoculum management practices. We discuss the constraints and opportunities toward overcoming the challenges associated with managing foliar disease complexes in field pea.

Combining Experimental and Modeling Approaches to Understand Genotype x Sowing Date x Environment Interaction Effects on Emergence Rates and Grain Yield of Soybean.

Soybean emergence and yield may be affected by many factors. A better understanding of the cultivar x sowing date x environment interactions could shed light into the competitiveness of soybean with other crops, notably, to help manage major biotic and abiotic factors that limit soybean production. We conducted a 2-year field experiments to measure emergence dynamics and final rates of three soybean cultivars from different maturity groups, with early and conventional sowing dates and across three locations. We also measured germination parameter values of the three soybean cultivars from different maturity groups under controlled experiments to parametrize the SIMPLE crop emergence model. This allowed us to assess the prediction quality of the model for emergence rates and to perform simulations. Final emergence rates under field conditions ranged from 62% to 92% and from 51% to 94% for early and conventional sowing, respectively. The model finely predicted emergence courses and final rates (root mean square error of prediction (RMSEP), efficiency (EF), and mean deviation (MD) ranging between 2% to 18%, 0.46% to 0.99%, and -10% to 15%, respectively) across a wide range of the sowing conditions tested. Differences in the final emergence rates were found, not only among cultivars but also among locations for the same cultivar, although no clear trend or consistent ranking was found in this regard. Modeling suggests that seedling mortality rates were dependent on the soil type with up to 35% and 14% of mortality in the silty loam soil, due to a soil surface crust and soil aggregates, respectively. Non-germination was the least important cause of seedling mortality in all soil types (up to 3% of emergence losses), while no seedling mortality due to drought was observed. The average grain yield ranged from 3.1 to 4.0 t ha-1, and it was significantly affected by the irrigation regime (p < 0.001) and year (p < 0.001) but not by locations, sowing date or cultivars. We conclude that early sowing is unlikely to affect soybean emergence in South-West of France and therefore may represent an important agronomic lever to escape summer drought that markedly limit soybean yield in this region.

Revisiting Sustainability of Fungicide Seed Treatments for Field Crops.

The use of fungicide seed treatment (FST) is a very common practice worldwide. The purported effectiveness of many fungicides in providing broad-spectrum and systemic control of important diseases and the perception that FST reduces overall pesticide use, hence lowering environmental impacts, have greatly promoted the use of FST in the last five decades. Since there have been rapid advancements in the types, formulations, and application methods for seed treatments, there is a need to re-evaluate the benefits versus the risks of FST as a practice. While the use of seeds treated with neonicotinoid insecticides has come under scrutiny due to concern over potential nontarget effects, there are knowledge gaps on potential negative impacts of FST on operators' (those who apply, handle, and use treated seeds) health and nontarget soil organisms (both macro- and microorganisms). Here we review existing knowledge on key fungicides used for seed treatments, benefits and risks related to FST, and propose recommendations to increase benefits and limit risks related to the use of FST. We found FST is applied to almost 100% of sown seeds for the most important arable crops worldwide. Fungicides belonging to 10 chemical families and with one or several types of mobility (contact, locally systemic, and xylem mobile) are used for seed treatment, although the majority are xylem mobile. Seed treatments are applied by the seed distributor, the seed company, and the farmer, although the proportion of seed lots treated by these three groups vary from one crop to another. The average quantity of fungicide active ingredient (a.i.) applied via seed treatment depends on the crop species, environment(s) into which seed is planted, and regional or local regulations. Cost-effectiveness, protection of the seed and seedlings from pathogens up to 4-5 weeks from sowing, user friendliness, and lower impact on human health and nontarget soil organisms compared with foliar spray and broadcast application techniques, are among the most claimed benefits attributed to FST. In contrast, inconsistent economic benefits, development of resistance by soilborne pathogens to many fungicides, exposure risks to operators, and negative impacts on nontarget soil organisms are the key identified risks related to FST. We propose eight recommendations to reduce risks related to FST and to increase their benefits.

Understanding Why Effective Fungicides Against Individual Soilborne Pathogens Are Ineffective with Soilborne Pathogen Complexes.

Annual forage legumes across southern Australia continue to be devastated by soilborne diseases. Nine fungicide seed treatments (thiram, metalaxyl, iprodione, phosphonic acid, propamocarb, fluquinconazole, difenoconazole + metalaxyl, ipconazole + metalaxyl, sedaxane + difenoconazole + metalaxyl) and four foliar fungicide treatments (phosphonic acid, metalaxyl, propamocarb, iprodione) were tested on four subterranean clover cultivars against individual oomycete soilborne pathogens Pythium irregulare, Aphanomyces trifolii, and Phytophthora clandestina and the fungal pathogen Rhizoctonia solani. Best treatments were then further tested across southern Australia in 2 years of field experiments. Under controlled conditions, seed treatment with thiram was best against damping-off caused by P. irregulare across the four cultivars (Woogenellup, Riverina, Seaton Park, Meteora), while metalaxyl was the most effective for maximizing root and shoot weights. Against A. trifolii, metalaxyl, iprodione, difenoconazole + metalaxyl, ipconazole + metalaxyl, and sedaxane + difenoconazole + metalaxyl, all reduced damping-off; sedaxane + difenoconazole + metalaxyl, fluquinconazole, and ipconazole + metalaxyl all reduced lateral root disease across two or more cultivars; while iprodione, thiram, and sedaxane + difenoconazole + metalaxyl increased shoot dry weight. Against P. clandestina, metalaxyl was the most effective in reducing tap and lateral root rot followed by ipconazole + metalaxyl or phosphonic acid for tap and lateral rot, respectively. Against R. solani, there were no effects of fungicides. For P. irregulare and P. clandestina, there were strong seed fungicide × cultivar interactions (P < 0.001). Under controlled conditions for foliar fungicide spray treatments, phosphonic acid was best at preventing productivity losses from A. trifolii, but was ineffective against P. clandestina, P. irregulare, or R. solani. Overall, controlled environment studies highlighted strong potential for utilizing seed treatments against individual pathogens to ensure seedling emergence and early survival, with seed and foliar sprays enhancing productivity by reducing seedling damping-off and root disease from individual pathogens. However, in field experiments over 2 years across southern Australia against naturally occurring soilborne pathogen complexes involving these same pathogens, only rarely did fungicide seed treatments or foliar sprays tested show any benefit. It is evident that currently available fungicide seed and/or foliar spray treatment options do not offer effective field mitigation of damping-off and root disease on annual forage legumes that underpin livestock production across southern Australia. The main reason for this failure relates to the unpredictable and ever-changing soilborne pathogen complexes involved, highlighting a need to now refocus away from fungicide options, particularly toward developing and deploying new host tolerances, but also in deploying appropriate cultural control options.

IPSIM-Web, An Online Resource for Promoting Qualitative Aggregative Hierarchical Network Models to Predict Plant Disease Risk: Application to Brown Rust on Wheat.

A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohen's κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.

GMDPtoolbox: A Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease.

Designing management policies in ecology and agroecology is complex. Several components must be managed together while they strongly interact spatially. Decision choices must be made under uncertainty on the results of the actions and on the system dynamics. Furthermore, the objectives pursued when managing ecological systems or agroecosystems are usually long term objectives, such as biodiversity conservation or sustainable crop production. The framework of Graph-Based Markov Decision Processes (GMDP) is well adapted to the qualitative modeling of such problems of sequential decision under uncertainty. Spatial interactions are easily modeled and integrated control policies (combining several action levers) can be designed through optimization. The provided policies are adaptive, meaning that management actions are decided at each time step (for instance yearly) and the chosen actions depend on the current system state. This framework has already been successfully applied to forest management and invasive species management. However, up to now, no "easy-to-use" implementation of this framework was available. We present GMDPtoolbox, a Matlab toolbox which can be used both for the design of new management policies and for comparing policies by simulation. We provide an illustration of the use of the toolbox on a realistic crop disease management problem: the design of long term management policy of blackleg of canola using an optimal combination of three possible cultural levers. This example shows how GMDPtoolbox can be used as a tool to support expert thinking.

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases.

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

Genome structure and reproductive behaviour influence the evolutionary potential of a fungal phytopathogen.

Modern agriculture favours the selection and spread of novel plant diseases. Furthermore, crop genetic resistance against pathogens is often rendered ineffective within a few years of its commercial deployment. Leptosphaeria maculans, the cause of phoma stem canker of oilseed rape, develops gene-for-gene interactions with its host plant, and has a high evolutionary potential to render ineffective novel sources of resistance in crops. Here, we established a four-year field experiment to monitor the evolution of populations confronted with the newly released Rlm7 resistance and to investigate the nature of the mutations responsible for virulence against Rlm7. A total of 2551 fungal isolates were collected from experimental crops of a Rlm7 cultivar or a cultivar without Rlm7. All isolates were phenotyped for virulence and a subset was genotyped with neutral genetic markers. Virulent isolates were investigated for molecular events at the AvrLm4-7 locus. Whilst virulent isolates were not found in neighbouring crops, their frequency had reached 36% in the experimental field after four years. An extreme diversity of independent molecular events leading to virulence was identified in populations, with large-scale Repeat Induced Point mutations or complete deletion of AvrLm4-7 being the most frequent. Our data suggest that increased mutability of fungal genes involved in the interactions with plants is directly related to their genomic environment and reproductive system. Thus, rapid allelic diversification of avirulence genes can be generated in L. maculans populations in a single field provided that large population sizes and sexual reproduction are favoured by agricultural practices.

Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans.

The Shtienberg model for predicting yield loss caused by Phytophthora infestans in potato was developed and parameterized in the 1990s in North America. The predictive quality of this model was evaluated in France for a wide range of epidemics under different soil and weather conditions and on cultivars different than those used to estimate its parameters. A field experiment was carried out in 2006, 2007, 2008, and 2009 in Brittany, western France to assess late blight severity and yield losses. The dynamics of late blight were monitored on eight cultivars with varying types and levels of resistance. The model correctly predicted relative yield losses (efficiency = 0.80, root mean square error of prediction = 13.25%, and bias = -0.36%) as a function of weather and the observed disease dynamics for a wide range of late blight epidemics. In addition to the evaluation of the predictive quality of the model, this article provides a dataset that describes the development of various late blight epidemics on potato as a function of weather conditions, fungicide regimes, and cultivar susceptibility. Following this evaluation, the Shtienberg model can be used with confidence in research and development programs to better manage potato late blight in France.