Special Issue "Mathematical Modelling Applications in Crop Ecology and Disease Epidemiology"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: closed (20 November 2019).

Special Issue Editor

Prof. Dr. Michael J Jeger
Website
Guest Editor
Centre for Environmental Policy, Imperial College London, Silwood Park, Ascot SL5 7PY, United Kingdom
Interests: plant disease epidemiology; mathematical modelling; network analysis of disease spread; modelling biological control; declines and complex diseases; insect-vectored plant diseases; pest risk assessment

Special Issue Information

Dear colleagues,

Crop ecology encompasses a wide spectrum of conceptual, scientific and technological approaches to improving crop productivity and quality, subject to multiple criteria set by environmental, socioeconomic, human and geopolitical constraints.  A major consideration in crop ecology research has been on crop performance and improvement in relation to the abiotic environment: climate, water use, soil structure and fertility. In these areas, mathematical modelling has been applied in identifying some of the key physiological determinants of crop performance and in predicting phenotypic consequences of genetic improvement. Mathematical modelling techniques have also been applied in plant disease epidemiology: directed at a fundamental understanding of epidemic dynamics, forecasting disease development, or optimization of disease management options. It has proved more of a challenge to address questions concerning the biotic, including human, interactions manifest in all cropping systems, whether arable crops, horticulture, grassland and pasture, energy crops or agroforestry systems, that influence disease epidemiology. Examples of such interactions include:

  • Crop productivity and heterogeneity in time and space
  • Landscape-cropping system interactions
  • Crop-natural vegetation/weed interactions
  • Phyllosphere ecology and biological control
  • Soil microbiology, rhizosphere ecology and plant health
  • Vector ecology and epidemiology
  • Deployment of host resistance in different cropping systems
  • Farmer/producer choices/preferences in disease management
  • Policy and regulatory drivers, including risk management

This Special Issue will focus on ‘Mathematical modelling applications in crop ecology and disease epidemiology” with an emphasis on biotic interactions, including the human and policy dimensions noted above. We welcome novel research, reviews and opinion pieces covering these and related topics, with an emphasis on the application of mathematical models rather than early model development. Mathematical modelling is interpreted in a broad sense to include simple rules and criteria developed as decision aids (e.g. Decision Support Systems), new statistical applications (e.g. meta-analysis, optimization, and Bayesian methods), biophysical models (e.g. aerobiology), complex systems (e.g. hierarchical simulation models), network analysis (e.g. landscape connectivity), and behavioral analysis (e.g. game theory).

Prof. Dr. Michael J Jeger
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical models
  • crop ecology
  • biotic interactions
  • cropping systems
  • pests, weeds and diseases
  • soil microbiology
  • farmer behavior
  • policy

Published Papers (5 papers)

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Research

Open AccessArticle
Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence
Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560 - 13 Apr 2020
Abstract
Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. [...] Read more.
Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs. Full article
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Open AccessArticle
A Generic Model Accounting for the Interactions among Pathogens, Host Plants, Biocontrol Agents, and the Environment, with Parametrization for Botrytis cinerea on Grapevines
Agronomy 2020, 10(2), 222; https://doi.org/10.3390/agronomy10020222 - 04 Feb 2020
Cited by 1
Abstract
Although the use of biocontrol agents (BCAs) to manage plant pathogens has emerged as a sustainable means for disease control, global reliance on their use remains relatively insignificant and the factors influencing their efficacy remain unclear. In this work, we further developed an [...] Read more.
Although the use of biocontrol agents (BCAs) to manage plant pathogens has emerged as a sustainable means for disease control, global reliance on their use remains relatively insignificant and the factors influencing their efficacy remain unclear. In this work, we further developed an existing generic model for biocontrol of foliar diseases, and we parametrized the model for the Botrytis cinerea–grapevine pathosystem. The model was operated under three climate types to study the combined effects on BCA efficacy of four factors: (i) BCA mechanism of action, (ii) timing of BCA application with respect to timing of pathogen infection (preventative vs. curative), (iii) temperature and moisture requirements for BCA growth, and (iv) BCA survival capability. All four factors affected biocontrol efficacy, but factors iii and iv accounted for > 90% of the variation in model simulations. In other words, the most important factors affecting BCA efficacy were those related to environmental conditions. These findings indicate that the environmental responses of BCAs should be considered during their selection, BCA survival capability should be considered during both selection and formulation, and weather conditions and forecasts should be considered at the time of BCA application in the field. Full article
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Open AccessArticle
Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity
Agronomy 2019, 9(9), 565; https://doi.org/10.3390/agronomy9090565 - 19 Sep 2019
Cited by 2
Abstract
Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect [...] Read more.
Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect the accuracy of the resulting mean disease severity for the sample. The aim of this study was to compare mean estimates based on various quantitative ordinal scale designs to the nearest percent estimates, and to investigate the effect of the number of classes in an ordinal scale on the accuracy of that mean. A simulation method was employed. The criterion for comparison was the mean squared error of the mean disease severity for each of the different scale designs used. The results indicate that scales with seven or more classes are preferable when actual mean disease severities of 50% or less are involved. Moreover, use of an amended 10% quantitative ordinal scale with additional classes at low severities resulted in a more accurate mean severity compared to most other scale designs at most mean disease severities. To further verify the simulation results, estimates of mean severity of pear scab on samples of leaves from orchards in Taiwan demonstrated similar results. These observations contribute to the development of plant disease assessment scales to improve the accuracy of estimates of mean disease severities. Full article
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Open AccessArticle
Evaluation of the ‘Irish Rules’: The Potato Late Blight Forecasting Model and Its Operational Use in the Republic of Ireland
Agronomy 2019, 9(9), 515; https://doi.org/10.3390/agronomy9090515 - 06 Sep 2019
Abstract
Potato late blight caused by Phytophthora infestans is one of the most important plant diseases known, requiring high pesticide inputs to prevent disease occurrence. The disease development is highly dependent on weather conditions, and as such, several forecasting schemes have been developed worldwide [...] Read more.
Potato late blight caused by Phytophthora infestans is one of the most important plant diseases known, requiring high pesticide inputs to prevent disease occurrence. The disease development is highly dependent on weather conditions, and as such, several forecasting schemes have been developed worldwide which seek to reduce the inputs required to control the disease. The Irish Rules, developed in the 1950s and calibrated to accommodate the meteorological network, the characteristics of potato production and the P. infestans population at the time, is still operationally utilized by the national meteorological agency, Met Éireann. However, numerous changes in the composition and dynamics of the pathosystem and the risks of production/economic consequences associated with potato late blight outbreaks have occurred since the inception of the Irish Rules model. Additionally, model and decision thresholds appear to have been selected ad hoc and without a clear criteria. We developed a systematic methodology to evaluate the model using the empirical receiver operating curve (ROC) analysis and the response surface methodology for the interpretation of the results. The methodology, written in the R language, is provided as an open, accessible and reproducible platform to facilitate the ongoing seasonal re-evaluation of the Irish Rules and corresponding decision thresholds. Following this initial analysis, based on the available data, we recommend the reduction of the thresholds for relative humidity and an initial period duration from 90% and 12 h to 88% and 10 h, respectively. Contrary to recent reports, we found that the risk of blight epidemics remains low at temperatures below 12 °C. With the availability of more comprehensive outbreak data and with greater insight into the founder population to confirm our findings as robust, the temperature threshold in the model could potentially be increased from 10 °C to 12 °C, providing more opportunities for reductions of pesticide usage. We propose a dynamic operational decision threshold between four and 11 effective blight hours (EBH) set according to frequency of the disease outbreaks in the region of interest. Although the risk estimation according to the new model calibrations is higher, estimated chemical inputs, on average, are lower than the usual grower’s practice. Importantly, the research outlined here provides a robust and reproducible methodological approach to evaluate a semi-empirical plant disease forecasting model. Full article
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Open AccessArticle
Morphological Description and Classification of Wheat Kernels Based on Geometric Models
Agronomy 2019, 9(7), 399; https://doi.org/10.3390/agronomy9070399 - 18 Jul 2019
Cited by 6
Abstract
Modern automated and semi-automated methods of shape analysis depart from the coordinates of the points in the outline of a figure and obtain, based on artificial vision algorithms, descriptive parameters (i.e., the length, width, area, and circularity index). These methods omit an important [...] Read more.
Modern automated and semi-automated methods of shape analysis depart from the coordinates of the points in the outline of a figure and obtain, based on artificial vision algorithms, descriptive parameters (i.e., the length, width, area, and circularity index). These methods omit an important factor: the resemblance of the examined images to a geometric figure. We have described a method based on the comparison of the outline of seed images with geometric figures. The J index is the percentage of similarity between a seed image and a geometric figure used as a model. This allows the description and classification of wheat kernels based on their similarity to geometric models. The figures used are the ellipse and the lens of different major/minor axis ratios. Kernels of different species, subspecies and varieties of wheat adjust to different figures. A relationship is found between their ploidy levels and morphological type. Kernels of diploid einkorn and ancient tetraploid emmer varieties adjust to the lens and have curvature values in their poles superior to modern “bread” varieties. Kernels of modern varieties (hexaploid common wheat) adjust to an ellipse of aspect ratio = 1.6, while varieties of tetraploid durum and Polish wheat and hexaploid spelt adjust to an ellipse of aspect ratio = 2.4. Full article
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