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Modelling
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Innovative Modelling Approaches in Agricultural Systems and Food Processes
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Innovative Modelling Approaches in Agricultural Systems and Food Processes

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 9193

Special Issue Editors

Dr. Jose A. Egea

E-Mail Website
Guest Editor
Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), Campus Universitario de Espinardo, E-30100 Murcia, Spain
Interests: modelling and optimization of agri-food processes; optimization in engineering and biotechnology; parameter estimation; optimal experimental design; data analysis; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Dr. Alberto Garre

E-Mail Website
Guest Editor
Food Microbiology, Wageningen University & Research, P.O. Box 17, 6700 AA, Wageningen, The Netherlands
Interests: the application and development of kinetic models; stochastic modelling; the application of other modelling approaches (e.g. network-based models); optimal experiment design; development of scientific software
Dr. Alejandro Galindo

E-Mail Website1 Website2
Guest Editor
Department of Agroforestry Science, University of Seville, Ctra. Utrera Km 1, 41013 Seville, Spain
Interests: irrigation management; deficit irrigation; climate change; plant ecophysiology; water stress; water relations; water footprint; water use efficiency; water productivity; water saving; droughts and water scarcity; plant nutrition; evapotranspiration and plant modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pablo S. Fernández-Escamez

E-Mail Website
Guest Editor
Universidad Politécnica de Cartagena, Paseo Alfonso XIII 48, 30203 Cartagena, Spain
Interests: food safety; microbial predictive modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Agricultural and food systems management is crucial to ensure feeding of a growing world population while keeping or even improving key issues like health, safety and environmental sustainability. These tasks are already being especially challenging due to climate change. While important advances in the last century have been done regarding quantification and mathematical modelling of agri-food processes, the continuous development of basic science opens new gaps to be covered. Agri-food systems are biological systems with an inherent dynamic and non-linear nature. Interactions among different systems and with the environment make the modelling process quite complex. Besides, different scales can be found when studying these systems: from the molecular level to the design of industrial processes, including the implementation of economic and/or social policies, what advice the use and development of integrated modelling approaches that account for the different scales and interactions. This inherent complexity turns mathematical models into an essential tool to understand these systems. This is specially the case during the last years, when the widespread use of sensors have increased the amount of data available for understanding these systems. This has enabled the application of Big Data & Machine Learning technologies to gain further insight in agri-food systems.

This Special Issue aims to develop and explore modelling tools applicable to agricultural and food systems to support researchers in different areas, food scientists, engineers, and agri-food policy managers. It is not limited to the development and validation of novel mathematical models, but covers every step in the modelling process. For instance, (optimal) experiment design, decision-aiding tools, optimization, big data & machine learning, or software development are also of interest.


Dr. Jose A. Egea
Dr. Alberto Garre
Dr. Alejandro Galindo
Prof. Dr. Pablo S. Fernández-Escamez

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Modelling is an international peer-reviewed open access quarterly 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 1000 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

  • Agri-food systems
  • Modelling
  • Simulation
  • Data analysis
  • Dynamic models
  • Integrated modelling
  • Multi-scale modelling
  • Software

Published Papers (3 papers)

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Research

23 pages, 3323 KiB  
Article
Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework
by John C. Chrispell, Eleanor W. Jenkins, Kathleen R. Kavanagh and Matthew D. Parno
Modelling 2021, 2(4), 753-775; https://doi.org/10.3390/modelling2040040 - 15 Dec 2021
Viewed by 2182
Abstract
Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather [...] Read more.
Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions. Full article
(This article belongs to the Special Issue Innovative Modelling Approaches in Agricultural Systems and Food Processes)
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18 pages, 6921 KiB  
Article
Model Based Design and Validation of a Batch Ohmic Heating System
by Oluwaloba Oluwole-ojo, Hongwei Zhang, Martin Howarth and Xu Xu
Modelling 2021, 2(4), 641-658; https://doi.org/10.3390/modelling2040034 - 15 Nov 2021
Viewed by 3010
Abstract
Using moderate electric field (MEF) techniques, Ohmic heating (OH) provides the rapid and uniform heating of food products by applying electric fields to them. A range of theoretical Ohmic heating models have been studied by researchers, but model validation and comparisons using experimental [...] Read more.
Using moderate electric field (MEF) techniques, Ohmic heating (OH) provides the rapid and uniform heating of food products by applying electric fields to them. A range of theoretical Ohmic heating models have been studied by researchers, but model validation and comparisons using experimental data and model development using system identification techniques from experimental data have not been evaluated. In this work, numerical models, mathematical models, and system identification models for an MEF process were developed. The MEF models were developed and simulated using COMSOL and MATLAB/Simulink software. When simulated, the developed models showed a volumetric rise in the overall food temperature. It was found that upon the application of an electric field, the resultant temperature depends on the electrical conductivity, product temperature, and magnitude of the electric field. For this reason, a systematic approach was used to validate the developed models. Experimental data derived from a commercially available batch Ohmic heater from C-Tech Innovation were used to validate the simulated models. Validation, analysis, and model comparison were conducted to compare developed models with experimental data. The validated simulated model helped improve the understanding of the effect of different critical process parameters of foods with a range of initial conditions. The validated model could accurately predict the temperature of heating under varying electric fields and food products with different thermo–physical properties. Full article
(This article belongs to the Special Issue Innovative Modelling Approaches in Agricultural Systems and Food Processes)
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19 pages, 4377 KiB  
Article
DESIDS: An Integrated Decision Support System for the Planning, Analysis, Management and Rehabilitation of Pressurised Irrigation Distribution Systems
by Abdelouahid Fouial and Juan Antonio Rodríguez Díaz
Modelling 2021, 2(2), 308-326; https://doi.org/10.3390/modelling2020016 - 28 May 2021
Cited by 2 | Viewed by 2652
Abstract
Pressurized irrigation distribution systems (PIDSs) play a vital role in irrigation intensification, especially in the Mediterranean region. The design, operation and management of these systems can be complex, as they involve several intertwined processes which need to be considered simultaneously. For this reason, [...] Read more.
Pressurized irrigation distribution systems (PIDSs) play a vital role in irrigation intensification, especially in the Mediterranean region. The design, operation and management of these systems can be complex, as they involve several intertwined processes which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested. This DSS has been developed to bear in mind the needs of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans which provide the best services to farmers. Hence, four modules were integrated in DESIDS: (i) irrigation demand and scheduling module; (ii) hydraulic analysis module; (iii) operation and management module; and (iv) design and rehabilitation module. DESIDS was tested on different case studies, proving itself a valuable tool for irrigation district managers, as it provides a wide range of decision options for the proper operation and management of PIDSs. The developed DSS can be used as a platform for future integrations and expansions, and to include other processes needed for better decision-making support. Full article
(This article belongs to the Special Issue Innovative Modelling Approaches in Agricultural Systems and Food Processes)
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