Modelling Plant Diseases for Decision Making in Crop Protection

Rossi, Vittorio; Giosuè, Simona; Caffi, Tito

doi:10.1007/978-90-481-9277-9_15

Cited by 52 publications

(55 citation statements)

References 33 publications

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“…Rossi et al [85] developed a decision support system (DSS) to determine the influence of environmental and agronomic factors (including host species and variety, previous crop, and type of soil tillage before sowing) on Fusarium head blight incidence and mycotoxin risk in small grain cereal; the DSS was validated with an independent dataset. All of these systems are empirical (they were developed based on field data), which results in the following limitations [58]: (i) they provide low detail in describing the pathosystem; (ii) they are difficult to extrapolate to different locations or pathosystems; and (iii) they have structures that cannot be extended with new knowledge. The mechanistic model described here is flexible and can be adapted to specific pathosystems by considering in each case the most important epidemiological components and the most common management actions.…”

Section: Discussionmentioning

confidence: 99%

“…Empirically measuring the effect of each resistance component on the epidemic development is possible only by performing monocyclic experiments under environmentally controlled experiments, because the overlapping of infection cycles in natural epidemics makes such determinations impossible. For understanding natural epidemics, modelling enables simulation of the effect of each component on epidemiological processes as the epidemic progresses [58][59][60]. In Figure 6A, the model parameters were initially set as in Figure 2 to simulate a susceptible variety and were progressively changed to mimic the single and combined effects of an HR variety, in which p = 8, i = 10, N = 0.6, and I = 0.6.…”

Section: Plant Resistancementioning

confidence: 99%

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A General Model for the Effect of Crop Management on Plant Disease Epidemics at Different Scales of Complexity

et al. 2020

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A general and flexible model was developed to simulate progress over time of the epidemics caused by a generic polycyclic pathogen on aerial plant parts. The model includes all of the epidemiological parameters involved in the pathogen life cycle: between-season survival, production of primary inoculum, occurrence of primary infections, production and dispersal of secondary inoculum both inside and outside the crop, and concatenation of secondary infection cycles during the host’s growing season. The model was designed to include the effect of the main crop management actions that affect disease levels in the crop. Policy-oriented, strategic, and tactical actions were considered at the different levels of complexity (from the agro-ecosystem to the farming and cropping system). All effects due to disease management actions were translated into variations in the epidemiological components of the model, and the model quantitatively simulates the effect of these actions on epidemic development, expressed as changes in final disease and in the area under the disease progress curve. The model can help researchers, students and policy makers understand how management decisions (especially those commonly recommended as part of Integrated Pest Management programs) will affect plant disease epidemics at different scales of complexity.

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Section: Discussionmentioning

confidence: 99%

Section: Plant Resistancementioning

confidence: 99%

A General Model for the Effect of Crop Management on Plant Disease Epidemics at Different Scales of Complexity

et al. 2020

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“…the closeness of a predicted value to its ‘true’ value) and robustness (i.e. the capacity of the model to perform equally well across different epidemiological conditions) …”

Section: Discussionmentioning

confidence: 99%

Accurate prediction of black rot epidemics in vineyards using a weather-driven disease model

2016

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“…A detailed explanation of the mathematics behind the model is out of the scope of the article, but the interested reader can find further information in [43,45,46,47]. …”

Section: Epidemiological Models For Preventing Downy Mildewmentioning

confidence: 99%

VineSens: An Eco-Smart Decision-Support Viticulture System

Perez-Exposito

Fernández‐Caramés

Fraga‐Lamas

et al. 2017

Sensors

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This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine.

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Modelling Plant Diseases for Decision Making in Crop Protection

Cited by 52 publications

References 33 publications

A General Model for the Effect of Crop Management on Plant Disease Epidemics at Different Scales of Complexity

A General Model for the Effect of Crop Management on Plant Disease Epidemics at Different Scales of Complexity

Accurate prediction of black rot epidemics in vineyards using a weather-driven disease model

VineSens: An Eco-Smart Decision-Support Viticulture System

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