Dataset Information

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

ABSTRACT: Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

SUBMITTER: Aparicio J

PROVIDER: S-EPMC10792065 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Aparicio Johan J Gezan Salvador A SA Ariza-Suarez Daniel D Raatz Bodo B Diaz Santiago S Heilman-Morales Ana A Lobaton Juan J

Frontiers in plant science 20240103

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limi ...[more]

PMID: 38235208

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Project description:Pesticides can volatilize from treated soil to the atmosphere causing increased environmental pollution and human exposure. Exposure assessment to airborne pesticides requires reasonable predictions of pesticide emissions. Understanding the volatilization behavior due to changes in environmental conditions can help in assessing the risk uncertainty and designing better mitigation strategies. In this study, we developed a mechanistic model that can be used to predict the hourly volatilization emissions from pesticide-treated soil at different environmental conditions. Pesticide properties and local environmental conditions drive the transport processes at the soil-air interface within the model. The numerical model simultaneously calculates the soil fluxes of heat, moisture, and pesticide at the soil-air interface with inputs of hourly meteorological data. The initial condition of pesticide concentration in soil is obtained from the applied mass during treatment. The numerical model was compared with an analytical model and with field observations for a soil injected fumigant and two surface applied pesticides. The model performance of 14 pesticides under stagnant conditions against the Jury's analytical model showed reasonable agreement with values for the coefficient of determination (R2) ranging from 0.76 to 0.99. The model was a good predictor of the field-scale volatilization of a fumigant (1,3-dichloropropene) application when compared to observations (R =20.8 ). Both the timing of the peak and the temporal variability of the measured volatilization of the fumigant were captured by the model when the fumigant was incorporated at a depth of 46 cm in the soil column. The model also showed reasonable agreement with the measured volatilization of two surface-treated pesticides, though site-specific meteorological data was unavailable for these observations. The results indicate that the modeling approach could be a useful tool to evaluate the impact of location-specific meteorological conditions on the field volatility of pesticides and determine the emissions for risk assessment.

Dataset Information

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Publications

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Similar Datasets

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