Dataset Information

Coupling epidemiological and tree growth models to control fungal diseases spread in fruit orchards.

ABSTRACT: Agronomic practices can alter plant susceptibility to diseases and represent a promising alternative to the use of pesticides. Yet, they also alter crop quality and quantity so that the evaluation of their efficacy is not straightforward. Here we couple a compartmental epidemiological model for brown rot diffusion in fruit orchards with a fruit-tree growth model explicitly considering the role of agronomic practices over fruit quality. The new modelling framework permits us to evaluate, in terms of quantity and quality of the fruit production, management scenarios characterized by different levels of regulated deficit irrigation and crop load. Our results suggest that a moderate water stress in the final weeks of fruit development and a moderate fruit load provide effective control on the brown rot spreading, and eventually guarantee monetary returns similar to those that would be obtained in the absence of the disease.

SUBMITTER: Bevacqua D

PROVIDER: S-EPMC6560096 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Coupling epidemiological and tree growth models to control fungal diseases spread in fruit orchards.

Bevacqua Daniele D Génard Michel M Lescourret Françoise F Martinetti Davide D Vercambre Gilles G Valsesia Pierre P Mirás-Avalos Josè Manuel JM

Scientific reports 20190611 1

Agronomic practices can alter plant susceptibility to diseases and represent a promising alternative to the use of pesticides. Yet, they also alter crop quality and quantity so that the evaluation of their efficacy is not straightforward. Here we couple a compartmental epidemiological model for brown rot diffusion in fruit orchards with a fruit-tree growth model explicitly considering the role of agronomic practices over fruit quality. The new modelling framework permits us to evaluate, in terms ...[more]

PMID: 31186487

Similar Datasets

Project description:Mason bees (Osmia spp.) are efficient fruit tree pollinators that can be encouraged to occupy and breed in artificial nesting material. In sweet cherry orchards, they are occasionally used as an alternative managed pollinator as a replacement for or in addition to honey bees (Apis mellifera). Yet, the lack of practical guidelines on management practices, for example optimal stocking rates, for both mason bee nesting material and honey bees might compromise pollination service provision. In this study, we assessed the relationship between stocking rates (honey bee hives and mason bee nesting material) and the abundance of honey bees and mason bees in 17 sweet cherry (Prunus avium) orchards in Central Germany. We furthermore performed a pollination experiment to explore the interactive effect of mason bees and honey bees on sweet cherry fruit set. In the orchards, both honey bee and mason bee abundance increased with increasing stocking rates of hives or nesting material, respectively. Honey bee abundance increased linearly with stocking rates. In contrast, mason bee abundance asymptoted at 2-3 nesting boxes per ha, beyond which more boxes resulted in little increase in visitation rate. Our pollination experiment demonstrated that orchards were pollen limited, with only 28% of insect-pollinated flowers setting fruit versus 39% of optimally hand-pollinated flowers. Honey bees and mason bees enhanced sweet cherry fruit set, but only when both were present and not when either was present alone in an orchard. Our findings demonstrate that offering nesting material for mason bees and employing honey bee hives can enhance bee abundance in sweet cherry orchards. By increasing honey bee abundance in combination with enhanced mason bee abundance, farmers can substantially boost fruit set and potentially sweet cherry yield. To enhance pollination services, farmers should consider the benefits of increasing pollinator biodiversity as an immediate benefit to improve crop yields.

Project description:Conspiracy theories in social networks are considered to have adverse effects on individuals' compliance with public health measures in the context of a pandemic situation. A deeper understanding of how conspiracy theories propagate through social networks is critical for the development of countermeasures. The present work focuses on a novel approach to characterize the propagation of conspiracy theories through social networks by applying epidemiological models to Twitter data. A Twitter dataset was searched for tweets containing hashtags indicating belief in the "5GCoronavirus" conspiracy theory, which states that the COVID-19 pandemic is a result of, or enhanced by, the enrollment of the 5G mobile network. Despite the absence of any scientific evidence, the "5GCoronavirus" conspiracy theory propagated rapidly through Twitter, beginning at the end of January, followed by a peak at the beginning of April, and ceasing/disappearing approximately at the end of June 2020. An epidemic SIR (Susceptible-Infected-Removed) model was fitted to this time series with acceptable model fit, indicating parallels between the propagation of conspiracy theories in social networks and infectious diseases. Extended SIR models were used to simulate the effects that two specific countermeasures, fact-checking and tweet-deletion, could have had on the propagation of the conspiracy theory. Our simulations indicate that fact-checking is an effective mechanism in an early stage of conspiracy theory diffusion, while tweet-deletion shows only moderate efficacy but is less time-sensitive. More generally, an early response is critical to gain control over the spread of conspiracy theories through social networks. We conclude that an early response combined with strong fact-checking and a moderate level of deletion of problematic posts is a promising strategy to fight conspiracy theories in social networks. Results are discussed with respect to their theoretical validity and generalizability.

Dataset Information

Coupling epidemiological and tree growth models to control fungal diseases spread in fruit orchards.

Publications

Coupling epidemiological and tree growth models to control fungal diseases spread in fruit orchards.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets