Special Issue "Application of Decision Support Systems in Agriculture"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editor

Dr. Francisco Gutiérrez
E-Mail Website
Guest Editor
KU Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
Interests: human computer interaction; information visualization; decision support systems

Special Issue Information

Dear Colleagues, 

Decision support systems (DSSs) are used in agriculture to collect and analyze data from a variety of sources with the ultimate goal of providing end users with insight into their critical decision-making process. In particular, in the agriculture domain, these systems help farmers to solve complex issues related to crop production. In this sense, DSSs are key elements of modern agriculture. However, as these tools scale into data-extensive, real-time monitoring systems, the goals of these systems become more challenging (information overload, system design, data collection). Furthermore, DSS designers are also interested in making these systems more accessible to end users, comfortable to use, and user-friendly. This Special Issue covers current trends and future developments of decision support systems in the agriculture domain. We welcome all types of research articles, and the manuscripts should present novel and original work addressing key topics such as the following: 

  • DSS principles and concepts;
  • DSS tools, methods, and techniques;
  • DSS interface design;
  • DSS implementation and evaluation. 

Therefore, I would like to kindly invite you to submit your work to our journal's incoming Special Issue. Your valuable contributions will positively enlighten the current state of DSS in agriculture.

Dr. Francisco Gutiérrez
Guest Editor

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 papers will be 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. Agriculture 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.


  • Decision-support systems
  • DSS
  • Human–computer interaction
  • Decision-making
  • Precision agriculture
  • Precision farming
  • Data processing
  • Information visualization
  • Sensors
  • Data analysis
  • Sensors
  • Big data
  • Artificial intelligence
  • Explainable artificial intelligence
  • ICT
  • Intelligent user interfaces
  • Data-driven design
  • User-centered design
  • Predictive analytics
  • Deep learning

Published Papers (1 paper)

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Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments
Agriculture 2021, 11(11), 1166; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111166 - 19 Nov 2021
Viewed by 327
Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were [...] Read more.
Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments. Full article
(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.


Authors: Antonio Satriani 1*, Francesco Izzi 1, Ermann Ripepi 1,
Institute of Methodologies for Environmental Analysis, CNR, Tito, Potenza, Italy
Correspondence: [email protected]

Abstract: Water resource is available in increasingly limited quantities and the countries of the Mediterranean region are among the most exposed. Accordingly, its use in agriculture should be as efficient as possible and technological innovation can help to optimize the use of water in irrigation practice. The technological improvement of irrigation will be faster and more widespread if it meets the needs of the farmer in terms of cost and ease of use. In this paper an automatic smart irrigation system is presented, where the combined use of soil moisture sensors and irrigation equipment in an Internet of Things (IoT) approach, allows automatic irrigation control with the opportunity to make water use in agriculture more efficient. The system was developed using open-source technologies (OST). Specifically, for the hardware components, a small sized board computer Raspberry PI 3B was used together with a 4G LTE Wi-Fi router and a Modbus rs485 / USB converter. In an experimental field test, the system was used for the irrigation of crops to optimize water consumption.

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