scholarly journals Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium

2021 ◽  
Vol 7 (9) ◽  
pp. 777
Author(s):  
Moussa El Jarroudi ◽  
Fadia Chairi ◽  
Louis Kouadio ◽  
Kathleen Antoons ◽  
Abdoul-Hamid Mohamed Sallah ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Model Performance ◽  
Decision Support Tool ◽  
Weather Conditions ◽  
Probability Of Detection ◽  
Cercospora Beticola ◽  
Cercospora Leaf Spot ◽  
Support Tool ◽  
Infection Period ◽  
Using Data

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.

scholarly journals A decision support tool for rural water supply planning

2020 ◽  
Vol 10 (3) ◽  
pp. 447-457
Author(s):  
Joseph Cook ◽  
Jake Wagner ◽  
Gunnar Newell
Keyword(s):  
Decision Support ◽  
Water Supply ◽  
Current Model ◽  
Decision Support Tool ◽  
Community Members ◽  
Rural Water Supply ◽  
Support Tool ◽  
Rural Water ◽  
Supply Planning ◽  
Using Data

Abstract Over a dozen studies have examined how households who travel to collect water (about one-quarter of humanity) make choices about where and how much to collect. There is little evidence, however, that these studies have informed rural water supply planning in anything but a qualitative way. In this paper, we describe a new web-based decision support tool that planners or community members can use to simulate scenarios such as (1) price, quality, or placement changes of existing sources, (2) the closure of an existing source, or (3) the addition of a new source. We describe the analytical structure of the model and then demonstrate its possibilities using data from a recent study in rural Meru County, Kenya. We discuss some limits of the current model, and encourage readers and practitioners to explore it and suggest ways in which it could be improved or used most effectively.

scholarly journals BEETSOIL: a decision support tool for forecasting the impact of soil conditions on sugar beet harvest

2019 ◽  
Vol 191 ◽  
pp. 131-141
Author(s):  
Miguel A. Gabarron-Galeote ◽  
Jacqueline A. Hannam ◽  
Thomas Mayr ◽  
Patrick J. Jarvis
Keyword(s):  
Decision Support ◽  
Sugar Beet ◽  
Decision Support Tool ◽  
Soil Conditions ◽  
Support Tool ◽  
The Impact

scholarly journals Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes

2013 ◽  
Vol 16 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Daniel J. Karran ◽  
Efrat Morin ◽  
Jan Adamowski
Keyword(s):  
Wavelet Transforms ◽  
Model Performance ◽  
Probability Of Detection ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Non Linear ◽  
Using Data

Considering the popularity of using data-driven non-linear methods for forecasting streamflow, there has been no exploration of how well such models perform in climate regimes with differing hydrological characteristics, nor has the performance of these models, coupled with wavelet transforms, been compared for lead times of less than 1 month. This study compares the use of four different models, namely artificial neural networks (ANNs), support vector regression (SVR), wavelet-ANN, and wavelet-SVR in a Mediterranean, Oceanic, and Hemiboreal watershed. Model performance was tested for 1, 2 and 3 day forecasting lead times, measured by fractional standard error, the coefficient of determination, Nash–Sutcliffe model efficiency, multiplicative bias, probability of detection and false alarm rate. SVR based models performed best overall, but no one model outperformed the others in more than one watershed, suggesting that some models may be more suitable for certain types of data. Overall model performance varied greatly between climate regimes, suggesting that higher persistence and slower hydrological processes (i.e. snowmelt, glacial runoff, and subsurface flow) support reliable forecasting using daily and multi-day lead times.

scholarly journals Characterization of the Mycovirome from the Plant-Pathogenic Fungus Cercospora beticola

Viruses ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1915
Author(s):  
Yingxi Li ◽  
Mengke Zhou ◽  
Yizhou Yang ◽  
Qi Liu ◽  
Zongying Zhang ◽  
...  
Keyword(s):  
Sugar Beet ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Pathogenic Fungus ◽  
High Yield ◽  
Cercospora Beticola ◽  
Sequencing Analysis ◽  
Cercospora Leaf Spot ◽  
Potential Biocontrol Agent ◽  
Production Areas

Cercospora leaf spot (CLS) caused by Cercospora beticola is a devastating foliar disease of sugar beet (Beta vulgaris), resulting in high yield losses worldwide. Mycoviruses are widespread fungi viruses and can be used as a potential biocontrol agent for fugal disease management. To determine the presence of mycoviruses in C. beticola, high-throughput sequencing analysis was used to determine the diversity of mycoviruses in 139 C. beticola isolates collected from major sugar beet production areas in China. The high-throughput sequencing reads were assembled and searched against the NCBI database using BLASTn and BLASTx. The results showed that the obtained 93 contigs were derived from eight novel mycoviruses, which were grouped into 3 distinct lineages, belonging to the families Hypoviridae, Narnaviridae and Botourmiaviridae, as well as some unclassified (−)ssRNA viruses in the order Bunyavirales and Mononegavirales. To the best of our knowledge, this is the first identification of highly diverse mycoviruses in C. beticola. The novel mycoviruses explored in this study will provide new viral materials to biocontrol Cercospora diseases. Future studies of these mycoviruses will aim to assess the roles of each mycovirus in biological function of C. beticola in the future.

scholarly journals Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine

2021 ◽  
Author(s):  
Helen J Mayfield ◽  
Colleen L Lau ◽  
Jane E Sinclair ◽  
Samuel J Brown ◽  
Andrew Baird ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Bayesian Network ◽  
Model Building ◽  
Empirical Studies ◽  
Decision Support Tool ◽  
Vaccine Hesitancy ◽  
Local Data ◽  
Support Tool ◽  
Relevant Variables ◽  
Using Data

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thromobocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate the existing evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate the existing evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the structure of the model, relevant variables, data to be included, and how these data were used to inform the model. The model can be used as a decision support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it a useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be easily adaptable to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.

An application of the use of safe minimum standards in the conservation of livestock biodiversity

2006 ◽  
Vol 11 (1) ◽  
pp. 77-94 ◽  
Author(s):  
ADAM G. DRUCKER
Keyword(s):  
Decision Support ◽  
Sustainable Use ◽  
Policy Decision ◽  
Decision Support Tool ◽  
Cost Ratio ◽  
Support Tool ◽  
Benefit Cost Ratio ◽  
Livestock Sector ◽  
Animal Genetic Resource ◽  
Using Data

This paper adapts the safe minimum standard (SMS) approach so as to explore its use as a potential policy decision support tool that can be applied to issues related to the conservation and sustainable use of farm animal genetic resource (AnGR) diversity. Empirical SMS cost estimates are obtained using data from three AnGR economics case studies in Mexico and Italy. The findings support our hypothesis that the costs of implementing an SMS are low, both when compared with the size of subsidies currently being provided to the livestock sector (<1 percent of the total subsidy) and with regard to the benefits of conservation (benefit-cost ratio of >2.9).Nevertheless, despite providing a potentially useful AnGR conservation decision support tool, a critical assessment of the application reveals that a much more extensive quantification of the components required to determine SMS costs needs to be undertaken before this tool can be applied in practice.

scholarly journals A DATA MINING APPROACH TO FORECAST LATE ARRIVALS IN A TRANSHIPMENT CONTAINER TERMINAL

Transport ◽  
2014 ◽  
Vol 29 (2) ◽  
pp. 175-184 ◽  
Author(s):  
Claudia Pani ◽  
Paolo Fadda ◽  
Gianfranco Fancello ◽  
Luca Frigau ◽  
Francesco Mola
Keyword(s):  
Data Mining ◽  
Container Terminal ◽  
Weather Conditions ◽  
Time Of Arrival ◽  
Support Tool ◽  
Data Mining Approach ◽  
Decision Making Processes ◽  
Contractual Obligations ◽  
Using Data ◽  
Cart Model

One of the most important issues in Transhipment Container Terminal (TCT) management is to have fairly reliable and affordable predictions about vessel arrival. Terminal operators need to estimate the actual time of arrival in port in order to determine the daily demand for each work shift with greater accuracy. In this way, the resources required (human resources, equipment as well as spatial resources) can be allocated more efficiently. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) 24 hours before arrival, ship operators often have to revise it due to unexpected events like weather conditions, delay in a previous port and so on. For planners the decision-making processes related to this topic can sometimes be so complex without the support of suitable methodological tools. Specific models should be adopted, in a daily planning scenario, to provide a useful support tool in TCTs. In this study, we discuss an exploratory analysis of the data affecting delays registered at a Mediterranean TCT. We present some preliminary results obtained using data mining techniques and propose a Classification and Regression Trees (CART) model to reduce the range of uncertainty of ship arrivals in port. This approach is compulsory to manage vast amounts of unstructured data involved in estimating of vessel arrivals.

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