Comparison of the performance when using rain-gauge or radar-based rainfall measurements in the PROCULTURE model for estimating the risk of infection events in winter wheat by
Abstract
The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.
Keywords
- mechanistic model
- stochastic model
- integrated pest management
1. Introduction
Plant disease epidemics involve changes in disease intensity in a host population over time and space. Acquiring comprehensive information on this process is necessary to understanding the factors that cause epidemics. However, even a complete set of data on disease intensity does not automatically lead to insights into the epidemic process. Furthermore, the information regarding risk of disease needs to be communicated stakeholders who can subsequently take management decisions to protect the crop when risk of an epidemic is deemed high. Various mathematical models are used to summarize the essential features of the data or measurements of interest regarding disease development. Models for biological or physical processes can be developed using several methods. Empirical models are developed to describe an observed process, phenomenon, or relationship between variables using established statistical principles, and do not use previously developed theory or concepts to establish the relationship between the response variable and predictor variables. In contrast, mechanistic models are developed based on a theory, hypothesis, or concept of how a phenomenon or process occurs. Data are later considered after the mechanistic model is developed and might be used to improve the theory on which the model is based.
2. Challenges in predicting plant disease epidemic development
In many of the models that are discussed in this chapter, diseased individuals are grouped in three categories. After infection of the host takes place, the infected individual first goes through a phase where the disease develops and “grows” in the individual, but the infected individual does not produce propagules or infectious units. The infected individual is in a latent state. After the latent period, the infected individual becomes an infectious individual, meaning that it now produces infectious units that have the potential to cause subsequent infections. “Disease forecasting,” “disease prediction,” and the development of “disease warning systems” are activities familiar to plant disease epidemiologists [1, 2, 3, 4, 5, 6]. Having identified the factors that lead to epidemics, it is of great importance to use this information to provide a basis for the management of plant disease. The level of disease risk to which a crop is exposed may be influenced by many factors, some of these are beyond the control of growers, but some factors are integral components of crop production systems and can be managed to minimize that risk.
2.1. Seasonality and the disease cycle
Many cropping systems are cyclical or seasonal. With annual plants, the crop is planted and harvested at specific times each year. Planting a specific (or a few) genotype(s) results in an abrupt increase in population of susceptible individuals. While harvesting immediately decreases both the population of susceptible individuals and the population of latent, infectious individuals. In the period between harvest and planting, the pathogen has to survive either as propagules or on living or dead plant material left in the field, in the soil, or in other locations. Crops are exposed to a risk of infection from pathogens, the outcome of which is economic loss when the epidemic increases above a certain threshold, which results from reduction in both the quantity and quality of crop yield. In this chapter, we are interested in quantifying the risk of infection to which a crop is exposed as a basis for deciding whether intervention aimed at disease suppression is justified. Aspects of this process differ from pathogen to pathogen, from crop to crop, and from location to location [4]. Goulds and Polly [7] and Binns et al. [8] draw a distinction between crop protection based on either curative or preventative action. Without necessarily wishing to adhere rigidly to this dichotomy, it is nevertheless clear that in some cases, sample data are the most important components of the information on which decision making is based. In others, data relating to the host and the environment often play a more important role, and the evidence on which a decision is made about the need for appropriate control action is therefore likely to be more wide ranging. The first step in the formulation of a disease management strategy for any cropping system is to identify the most important risk factors among those on the long list of possible candidates. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate of the pathogen life cycle. To be able to identify risk factors, we need information both on the candidate risk factors and on the definitive status of the crops in which they are studied.
2.2. Basis of decision making
Jones [9] discussed a decision-making guideline based on impact on yield for fungicidal control of eyespot disease of winter wheat (
In this chapter we specifically provide an overview of different weather-based disease models developed and used for assessing in real time the risk of epidemic development for the major fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg. A description of the models is provided along with the constraints associated with their use for in-season disease monitoring. The challenges faced using weather-based models in a changing climate are also discussed.
3. Main fungal diseases of wheat in Luxembourg and associated decision support systems
Wheat represents one of the most widely cultivated cereals with a production area of 215 million ha worldwide [11]. Unfortunately, wheat diseases remain a major constraint to wheat production [12]. Crop protection often relies on calendar-date applied, preventive fungicide applications, and small grain cereals are typically treated with two or three foliar fungicide applications in Luxembourg and Belgium [13, 14]. The marginal cost/revenue ratio for winter wheat and the potential negative impacts that overuse of pesticides can have on the environment are compelling arguments to minimize inputs, including fungicides. Effective estimation of the risk of disease epidemic development can minimize the number of fungicide spray applied, leading to a more sustainable and environmentally friendly system of wheat production. Using tools to develop integrated pest management can lead to fungicides being applied only at particular stages that are at risk of infection, and only when the pathogen has been correctly identified (accurate identification and/or estimation of severity of disease can be critical to effective management). Diseases of wheat that have become economically important in Luxembourg include Septoria leaf blotch (SLB) caused by
3.1. Septoria leaf blotch
The majority of the SLB disease prediction systems proposed for the management of
Due to the potential for yield loss from SLB, growers tend to spray fungicides several times during the winter wheat season to protect their crops. The development of resistance in
However, many models neglect the periods of interruption of acceptable temperature or humidity for infection which are important factors in disease development, and can be an indispensable element in developing more accurate models. According to Shaw [38], interruptions in periods at 75% relative humidity for 48 h slightly reduced the efficiency of the infection process, but interruptions at 50% relative humidity resulted in major effects, but still allowed infection to occur. To simulate infection, some models take daily conditions [39, 40], while others, for example the PROCULTURE model are based on hourly weather conditions [14, 20].
3.1.1. The PROCULTURE model
The PROCULTURE model is an interactive web-based, field-specific, DSS based on the mechanistic modeling of the development of the last five leaf layers of the wheat plant coupled with the progress of SLB on these layers [14, 20, 41, 42]. A descriptive flowchart of the model is presented in Figure 1. The main inputs include weather data (hourly air temperature, rainfall and relative humidity) and field-specific data including the location, sowing date and cultivar susceptibility. Field observations are also important since a fine-tuning of the model may be required based on the actual growth stage (around the first node stage, GS32) and the severity of SLB on the particular leaf layer as specified by the model. The model considers infection to have occurred when, during a 2 h rainfall event, precipitation for the first hour is at least 0.1 mm (to allow for the swelling of pycnidia), followed by a second hour with at least 0.5 mm precipitation (Figure 1), leading to the release and splash dispersal of the conidia [14, 20]. In addition, after rainfall, relative humidity should be higher than 60% during the following 16 h [20, 43] and the temperature should remain above 4°C for 24 h [20] for germination and infection.
The evaluation of the PROCULTURE model at several sites in Belgium [14, 44] and Luxembourg [20] demonstrated that the model can explain disease progression in the canopy (Figure 2) and can be used to advise farmers when to apply fungicides during stem elongation, as the three upper leaves emerge. The need for and timing of a single fungicide spray using the PROCULTURE model is based on the observed disease severity earlier in the cropping season (i.e., severity on the lower leaves L5-L4 at GS 31–37, L1 being the flag leaf), the susceptibility of the cultivar, past and forecasted weather conditions, and the predicted development of leaves based on the output of the PROCULTURE model. Furthermore, historical data (weather and disease incidence and severities) were used as a basis for similarity analysis to further evaluate the risk of severe disease development. Given the threshold level of observed disease severity (namely on the lower leaves) and weather conditions (actual and forecasted), an advice for fungicide treatment was taken and fungicides applied only if required to protect the upper leaves. For example, a 5% of emergence of L3 coinciding with SLB symptoms on L5 and a rainfall event, results in a greater risk that L3 will be affected by SLB during full emergence. Consequently, a fungicide treatment against the risk of SLB is recommended if a latency period of the disease is completed at 75% emergence and favorable weather conditions forecasted. Overall, the assessment of the infection periods achieved an accuracy of 85%. The results showed that the PROCULTURE model satisfactorily recommended none or a single fungicide treatment at each study site, regardless of geographical location or possible variability among the fungal diseases involved [45].
3.1.2. Spatialization of PROCULTURE alerts using radar rainfall
The PROCULTURE model is being used in early warning systems in Belgium and Luxembourg to define, in real time, the risk of SLB developing on the upper leaves of winter wheat during stem elongation. However, setting up an operational network for recommending the optimal time for fungicide application requires a representative network of weather stations throughout the region where the DSS will be used. In our studies (e.g., [20, 46]) overestimation or underestimation of the risk of SLB progression could often be traced back to differences in rain events captured by the tipping-bucket rain-gauges at the weather station compared with the rainfall to which a particular field was actually exposed. Rainfall data could be interpolated between weather stations, but precipitation between fields are characterized by high spatial and temporal variability [47, 48], making the interpolation unreliable [49, 50].
Radar may provide a solution for improving the interpolation of precipitation data [51, 52]. Over the past few years, radar-derived estimates have been increasingly used in disease forecasting applications as an alternative to gauge-derived measurements [51, 53].
Mahtour et al. [42] validated the simulation of infection periods for
Field sites | Observation period | Year | Eventsa | Duration of infection periodb | PODsoc | FARsod | CSIsoe | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
Gauge | Radar | Gauge | Radar | Gauge | Radar | Gauge | Radar | ||||
HUMAINf | 21/05 to 05/07 | 2003 | 18 | 60 | 62 | 0.93 | 0.83 | 0 | 0 | 0.93 | 0.83 |
03/05 to 28/06 | 2004 | 16 | 46 | 40 | 0.73 | 0.87 | 0 | 0 | 0.73 | 0.87 | |
20/05 to 15/07 | 2005 | 8 | 24 | 27 | 0.86 | 1.00 | 0 | 0.12 | 0.85 | 0.87 | |
42 | 130 | 129 | |||||||||
USELDANGEg | 13/05 to 29/06 | 2003 | 15 | 56 | 44 | 0.87 | 0.80 | 0 | 0 | 0.87 | 0.80 |
16/05 to 09/07 | 2004 | 18 | 48 | 48 | 0.72 | 0.78 | 0 | 0 | 0.72 | 0.78 | |
14/05 to 12/07 | 2005 | 15 | 33 | 32 | 0.71 | 0.86 | 0.09 | 0.07 | 0.67 | 0.81 | |
48 | 137 | 124 | |||||||||
BURMERANGEg | 17/05 to 03/07 | 2003 | 10 | 30 | 22 | 0.70 | 0.70 | 0 | 0 | 0.70 | 0.70 |
05/05 to 13/07 | 2004 | 15 | 43 | 55 | 0.73 | 0.93 | 0 | 0 | 0.73 | 0.93 | |
12/05 to 04/07 | 2005 | 12 | 24 | 28 | 0.91 | 0.83 | 0 | 0 | 0.91 | 0.83 | |
37 | 97 | 105 | |||||||||
REULERg | — | 2003 | — | — | — | — | — | — | — | — | — |
27/05 to 05/07 | 2004 | 10 | 45 | 32 | 0.70 | 0.70 | 0 | 0 | 0.70 | 0.70 | |
16/05 to 11/07 | 2005 | 11 | 24 | 23 | 0.82 | 1.00 | 0 | 0 | 0.82 | 1.00 | |
21 | 69 | 55 | |||||||||
All | 148 | 433 | 413 | 0.79 | 0.84 | 0.01 | 0.02 | 0.77 | 0.83 |
3.2. Wheat leaf rust
WLR is of major historical significance and is of economic importance worldwide. It is the most widespread of the three species of rusts causing significant yield losses over large geographical areas [54, 55, 56, 57, 58, 59]. Several studies in major cereal-producing areas have revealed that epidemics of WLR occur under (i) favorable conditions for overwintering spores as a source of primary inoculum, (ii) rapid and abundant production of wind-dispersed urediniospores, and (iii) a complex interaction between environmental conditions and host resistance [54, 60]. The dispersal of foliar pathogens and WLR in particular around a spore source has been described in many studies, sometimes confirming dispersal over large distances [61] but most often at the spatial scale of an infected plant or group of plants [62, 63], or even a single leaf [64]. Although these studies give valuable insights to allow understanding of epidemic spread of diseases like WLR and to parameterize simulation models, they most often do not take into account the local structure of the host crop and its potential effect on disease distribution [64].
Two different approaches have been used to forecast development of epidemics of WLR. Some forecasting systems consider the effect of weather on the disease by means of empirical rules, flow charts [65], disease indices [66, 67], or regression equations [68, 69]. Other models forecast severity of WLR on the basis of the dynamic of the epidemic, using a fixed relative growth rate of the disease [70, 71, 72].
Moisture and temperature are reported to be the most important meteorological parameters influencing the development of epidemics of WLR [73]. Nevertheless, the genetic resistance of wheat cultivars is critically important factor in determining the impact of the disease [74]. Urediniospores are deposited by wind or rain on the adaxial and abaxial surfaces of wheat leaves. Rain on, or turbulence around the leaf surface allows the dispersal of urediniospores. In addition, wet deposition (spore scavenged from the air by rain) is considered an important mechanism of crop contamination by some rusts [75]. Although most rainfall events promote spore dispersal in the field, heavy rain may also induce the leaching of spores deposited on leaves and may totally deplete the lesions in the process [76]. When the urediniospores of WLR are in contact with susceptible wheat leaves, the success of infection requires a minimal duration of surface wetness, which varies as a function of temperature [50, 77]. De Vallavieille-Pope et al. [77] showed that optimum temperatures for uredospore germination ranged from 12 to 15°C and that the germination process ceased above 35°C. As noted, the presence of free water on the leaf surface is essential for urediniospore germination. In an earlier study, Eversmeyer [78] proposed an optimum temperature of 16°C for completion of the infection process by uredinisospores of
Considering these data, an empirical approach for simulating infection by WLR and progress of the disease on the upper three leaf layers has been proposed and validated in Luxembourg [2]. The model used only weather data logged between 8 pm to 5 am based on the assumption that spore germination is inhibited by light. Each infection event was deemed to require a period of at least 12 consecutive hours counted on at least two nights with air temperatures ranging between 8 and 16°C and a relative humidity greater than 60% (Figure 3). Moreover, the hourly rainfall totals during these 12-hour periods must be less than 1 mm to avoid the leaching of spores present on leaves. Furthermore, the primary infection in a field requires a light rain (0.1–1.0 mm) in the first hour of an infection event supposing that this rainfall allows the first deposition of the inoculum in the field. This light rain event is not a necessity once the primary infection has occurred. The model has led to a DSS that allows optimizing timing of applications of the fungicide for controlling WLR in fields in Luxembourg.
The presence of primary inoculum in the air is not considered as a limited factor in this model. We assumed that spores of
Thus, the detection of airborne inoculum by sensors and its coupling to a reliable model of dispersion could help improve forecasting the occurrence of WLR. In Belgium, a recent study on the spatio-temporal distribution of the airborne inoculum of
Molecular diagnostics combined with sampling of airborne inoculum could be exploited to more accurately predict the risk of epidemics in wheat agro-ecosystems. Strategies for controlling WLR in fields include the use of resistant cultivars. But a prolonged period of monitoring WLR involving susceptible cultivars and favorable night conditions conducive to spore production, dispersal of, and infection by
3.3. Wheat stripe rust
WSR is an example of a disease of world-wide importance and ability for long distance dispersal. Crop pathogens with worldwide prevalence and potential for long distance migration and thus invasions into new areas may pose a serious threat to food security regionally or globally [85]. WSR of wheat is among the most important crop diseases causing a continuous threat to crop production [86, 87]. Worldwide. the virulence and race diversity of populations of
In most seasons, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of
The model developed is based on a stepwise approach (Figure 5) consisting of (1) the determination of the potential range of weather conditions conducive to WSR in Luxembourg using a stochastic approach, and (2) the determination of optimum classes of combined weather variables (air temperature (T), relative humidity (RH) and rainfall (R)) conducive to the disease and building of a weather threshold based model for predicting WSR infection events [13].
The threshold-based model for development of WSR was evaluated using independent data from experiments in Luxembourg in 2002–2015 [13]. Infection days and latency periods for
3.4. Fusarium head blight
Besides the yield loss that it can cause, FHB can negatively affect the entire human food and animal feed chain through the contamination of wheat grains with mycotoxins. Contamination with fumonisins can result in grains unusable for consumption or for further processing into bakery products, breakfast cereals, pasta, snacks, beer or animal feed, etc., [101, 102, 103, 104, 105, 106].
Weather is a critical factor influencing FHB. Frequent rainfall, high humidity and warm temperatures, coinciding with flowering and early kernel filling, favor infection by
Many studies have highlighted the relationship between the severity of FHB in specific fields where certain cereals particularly maize, were the previous crop [103, 112]. Maize residues are a host for several
A simulation model for predicting the periods of infection by
4. Concluding remarks
Meteorological variables are most often used as the input data for disease forecasting models of fungal diseases of winter wheat in Luxembourg and elsewhere. For disease risk assessments at the regional scale, the meteorological data in these forecasting models must originate from local weather stations which are part of a meteorological networks consisting of automatic weather stations (AWSs). However, the choice of location for an AWS within a field or the distance between AWSs locations are both factors that hamper accurate forecasting of fungal diseases at regional scales. Moreover, techniques used to interpolate weather data from a set of neighboring sites suffer from some potential sources of error, e.g., difficulty in capturing small scale variation, failure to account for local topographical features, etc.
With the changes in the patterns of world climate expected during the coming decades [117], the pattern of corresponding distributions of fungal diseases will be affected accordingly. Thus, new challenges are emerging that need to be addressed. Climate change affects pathogen biology not only directly but also indirectly through effects on host development and phenology. Modeling to predict new disease threats is expected to be beneficial since many years of data are needed to prepare appropriate solutions to developing issues. However, although the impacts of climate change on crop disease are being studied, uncertainties inherent in crop disease models remain largely unexplored and unreported [118]. Moreover, acclimation to future climatic conditions by both the pathogen and the host can significantly alter the outcome of the plant–pathogen interaction [119].
Wheat diseases present a constant and evolving threat to food security. Decision-support tools based on in-season disease monitoring and disease progress models in relation to weather variables present various advantages for managing the development of epidemics of those diseases, while limiting potentially harmful side effects of excessive fungicide applications while ensuring economic benefit. Embedded in operational warning systems for plant disease monitoring, DSSs could provide a valuable service to the farmer community for pest and disease management through integrated and environmentally friendly methods.
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