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Exploring the Innovative Approaches and Advanced Practices to Sustainable Agriculture...
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Exploring the Innovative Approaches and Advanced Practices to Sustainable Agriculture and Animal Husbandry

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1309

Special Issue Editors

Dr. Waleed Al-Marzooqi

E-Mail Website
Guest Editor
Department of Animal & Veterinary Sciences, Sultan Qaboos University, Al- Khod P.O. Box 34, Oman
Interests: sustainable practices within the realms of agriculture and animal husbandry; the innovative methods, technologies, and management strategies of sustainable agriculture; agricultural and animal husbandry activities
Special Issues, Collections and Topics in MDPI journals
Dr. Hani El-Zaiat

E-Mail Website
Guest Editor
Department of Animal & Veterinary Sciences, Sultan Qaboos University, Al- Khod P.O. Box 34, Oman
Interests: herd productivity; nutritional and feeding challenges; diet quality and the ruminal fermentation process; ecological and biochemical functions of the ruminal ecosystem using phytogenic substances as natural feed additives; innovative nutritional approaches and alternative feed resources to reduce rumen methane production and enhancing animal performance and profitability in a sustainable manner
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable agriculture and animal husbandry is an interdisciplinary field that addresses the pressing need to develop agricultural practices that promote environmental stewardship, economic viability, and social equity. The scope of this Special Issue is to explore innovative approaches and research findings that contribute to the advancement of sustainable practices in agriculture and animal husbandry.

This Special Issue aims to encompass a wide range of topics within the realm of sustainable agriculture and animal husbandry, including, but not limited to, the following:

(a) Precision farming: Utilizing advanced technologies such as precision agriculture and data analytics to optimize resource use, reduce environmental impact, and improve the overall efficiency in both crop production and animal farming.

(b) Animal welfare: Addressing ethical considerations and promoting practices that ensure the well-being of livestock, with a focus on humane treatment, adequate living conditions, and responsible breeding.

(c) Climate change mitigation: Exploring strategies to mitigate the impact of agriculture and animal husbandry on climate change, including carbon sequestration, reduced greenhouse gas emissions, and adaptation measures.

Authors are encouraged to submit original research, reviews, and case studies that contribute to the scientific knowledge and practical implementation of sustainable practices in agriculture and animal husbandry. The ultimate purpose of this Special Issue is to disseminate valuable insights that can inform policymakers, researchers, and practitioners, fostering a transition towards more sustainable and resilient food production systems.

We look forward to receiving your contributions.

Dr. Waleed Al-Marzooqi
Dr. Hani El-Zaiat
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • sustainable agriculture
  • precision farming
  • animal welfare
  • climate change mitigation

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Published Papers (1 paper)

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Research

27 pages, 3573 KiB  
Article
Modelling of Carbon Monoxide and Suspended Particulate Matter Concentrations in a Rural Area Using Artificial Neural Networks
by Saleh M. Al-Sager, Saad S. Almady, Abdulrahman A. Al-Janobi, Abdulla M. Bukhari, Mahmoud Abdel-Sattar, Saad A. Al-Hamed and Abdulwahed M. Aboukarima
Sustainability 2024, 16(22), 9909; https://doi.org/10.3390/su16229909 - 13 Nov 2024
Viewed by 678
Abstract
Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, [...] Read more.
Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters. Full article
(This article belongs to the Special Issue Exploring the Innovative Approaches and Advanced Practices to Sustainable Agriculture and Animal Husbandry)
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