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Agronomy
Special Issues
Big Data for Agriculture Monitoring
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Big Data for Agriculture Monitoring

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 7684

Special Issue Editor

Prof. Dr. M-Tahar Kechadi

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: machine learning; neural intelligence; classification; pattern recognition; supervised learning; feature extraction; data collection; advanced learning; data discovery; unsupervised learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data collection in agriculture is becoming commonplace. With the proliferation of digital devices ranging from simple temperature sensor to complex digital weather stations, airborne instruments, and the Internet-of-Things (IoT), we collect huge amounts of data in space and time. The advances in data science and big data analytics have made smart farming or digital agriculture possible. In crop farming, many factors have a direct impact on both crop production and its ecosystem (cost, environment, etc.). For instance, excess fertilizer application is known to have a very negative impact on the environment (increased greenhouse gas emissions) and crop quality (an excess of certain elements can affect the uptake of other elements). With the large quantity of data currently available, we are finally able to study all of these factors and assess their impact on plant growth, yield, resource management, and the environment.

This Special Issue seeks submissions from academia, the industry, and governmental research labs, which present novel research on all theoretical and practical aspects related to the use of big data in agriculture.

Prof. Dr. M-Tahar Kechandi
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 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. 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 2600 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

  • digital agriculture
  • precision farming
  • big data analytics
  • agricultural data analysis
  • decision-making in agriculture

Published Papers (2 papers)

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Research

29 pages, 1621 KiB  
Article
Marginal Trade-Offs for Improved Agro-Ecological Efficiency Using Data Envelopment Analysis
by Tong Guang Ji, Ali Raza, Usman Akbar, Masood Ahmed, József Popp and Judit Oláh
Agronomy 2021, 11(2), 365; https://doi.org/10.3390/agronomy11020365 - 18 Feb 2021
Cited by 7 | Viewed by 2546
Abstract
Today’s agricultural management decisions impact food security and sustainable ecosystems, even when operating with back-to-basic operations. In such endeavors, policymakers usually need a quantitative tool, such as trade-offs margins, to effectively adjust resource consumption or production. This paper applies the weighted slack-based measurement [...] Read more.
Today’s agricultural management decisions impact food security and sustainable ecosystems, even when operating with back-to-basic operations. In such endeavors, policymakers usually need a quantitative tool, such as trade-offs margins, to effectively adjust resource consumption or production. This paper applies the weighted slack-based measurement (SBM-DEA) program to 136 developing countries’ agricultural performance. First, it finds the current agricultural efficiency and then makes marginal trade-offs on desirable-output variables (such as crop yield and forest area) to see the effective changes in undesirable-output (such as methane and nitrous oxide emissions). The results show that choosing effective marginal trade-offs does not deteriorate the relative efficiency of the decision-making units (DMUs) below the efficient frontier line. Thus, such a method enables the decision-makers to determine the best marginal trade-off points to reach the optimal efficiencies and decide which output factor needs special brainstorming to design effective policy. Full article
(This article belongs to the Special Issue Big Data for Agriculture Monitoring)
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12 pages, 3643 KiB  
Article
Rapid Acquisition, Management, and Analysis of Spatial Maize (Zea mays L.) Phenological Data—Towards ‘Big Data’ for Agronomy Transformation in Africa
by Henri E. Z. Tonnang, Tesfaye Balemi, Kenneth F. Masuki, Ibrahim Mohammed, Julius Adewopo, Adnan A. Adnan, Bester Tawona Mudereri, Bernard Vanlauwe and Peter Craufurd
Agronomy 2020, 10(9), 1363; https://doi.org/10.3390/agronomy10091363 - 10 Sep 2020
Cited by 9 | Viewed by 4334
Abstract
Mobile smartphones, open-source set tools, and mobile applications have provided vast opportunities for timely, accurate, and seamless data collection, aggregation, storage, and analysis of agricultural data in sub-Saharan Africa (SSA). In this paper, we advanced and demonstrated the practical use and application of [...] Read more.
Mobile smartphones, open-source set tools, and mobile applications have provided vast opportunities for timely, accurate, and seamless data collection, aggregation, storage, and analysis of agricultural data in sub-Saharan Africa (SSA). In this paper, we advanced and demonstrated the practical use and application of a mobile smartphone-based tool, i.e., the Open Data Kit (ODK), to assemble and keep track of real-time maize (Zea mays L.) phenological data in three SSA countries. Farmers, extension agents, researchers, and other stakeholders were enlisted to participate in an initiative to demonstrate the applicability of mobile smartphone-based apps and open-source servers for rapid data collection and management. A pre-installed maize phenology data application based on the ODK architecture was provided to the participants (n = 75) for maize data collection and management over the maize growing season period in 2015–2017. The application structure was custom designed based on maize developmental stages such as planting date, date of emergence, date of first flowering, anthesis, grain filling, and maturity. Results showed that in Ethiopia, early maturing varieties took 105 days from sowing to maturity in low altitudes, whereas late-maturing varieties took up to 190 days to complete developmental stages in high altitude areas. In Tanzania, a similar trend was observed, whereas in Nigeria, most existing varieties took an average of 100 days to complete their developmental stages. Furthermore, the data showed that the durations from sowing to emergence, emergence to flowering, flowering to maturity were mainly dependent on temperature. The values of growing degree for each phase of development obtained from different planting dates were almost constant for each maize variety, which showed that temperature and planting time are the main elements affecting the rate of maize development. The data aggregation approach using the ODK and on-farm personnel improved efficiency and convenience in data collection and visualization. Our study demonstrates that this system can be used in crop management and research on many spatial scales, i.e., local, regional, and continental, with relatively high data collation accuracy. Full article
(This article belongs to the Special Issue Big Data for Agriculture Monitoring)
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