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New Perspective of Artificial Intelligence and Data Analytics on the...
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New Perspective of Artificial Intelligence and Data Analytics on the Agricultural Land

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5973

Special Issue Editors

Dr. Rita X. Valenzuela

E-Mail Website
Guest Editor
CIEMAT Energy Department, Renewable Energy Division, Avda. Complutense 40, 28040 Madrid, Spain
Interests: solar resource; catalysis; materials; solar radiation measurement; calibration of equipment; generation and time series analysis
Dr. Francisco Ferrera-Cobos

E-Mail Website
Guest Editor
CIEMAT Energy Department, Renewable Energy Division, Avda. Complutense 40, 28040 Madrid, Spain
Interests: solar resource; photosynthetically active radiation; data analytics and measurement
Dr. Ignacio García Ruiz

E-Mail Website
Guest Editor
1. Institute of Smart Cities (ISC), Department of Engineering, Public University of Navarre, Campus Arrosadía, 31006 Pamplona, Spain
2. Research Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, University of Burgos, 09006 Burgos, Spain
Interests: solar radiation; photovoltaics; time series analysis; photosynthetically active radiation; agriculture; biomass

Special Issue Information

Dear Colleagues,

As our capacity to measure different variables at many locations simultaneously increases, so does the amount of raw data to collect and store. Therefore, it is necessary to resort to data analytics techniques in order to manage a large amount of data so that it could be possible to discern trends and relations among variables. In addition, it is not always possible to have access to data at some locations or it is too expensive to carry out a measurement campaign. In such cases, it is necessary to go to artificial intelligence or machine learning techniques to estimate or simulate a certain variable.

For this Special Issue, we are interested in contributions of artificial intelligence and data analytics on agricultural land, including but not limited to:

  • Simulating and modeling of key variables for agricultural use
  • Agrivoltaics
  • The use of machine learning techniques to estimate biomass production
  • The use of remote sensing applied to land/climate studies

However, contributions from other fields related to land–climate interactions, modeling variables of interest for cultures, or any other approach that links water, energy, land, and food are highly welcome. Regional or local studies are also desired.

Dr. Rita X. Valenzuela
Dr. Francisco Ferrera-Cobos
Dr. Ignacio García Ruiz
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. Land 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

  • remote sensing
  • agricultural use
  • machine learning
  • biomass production
  • data mining
  • agrivoltaics

Published Papers (2 papers)

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Research

32 pages, 2904 KiB  
Article
Development of a Systems Model for Assessing Pathways to Resilient, Sustainable, and Profitable Agriculture in New Zealand
by Clémence Vannier, Thomas A. Cochrane, Peyman Zawar-Reza and Larry Bellamy
Land 2022, 11(12), 2334; https://doi.org/10.3390/land11122334 - 19 Dec 2022
Cited by 1 | Viewed by 3854
Abstract
There is a clear research gap in understanding how future pathways and disruptions to the New Zealand (NZ) agricultural system will have an impact on the environment and productivity. Agriculture is in a period of significant change due to market disruptions, climate change, [...] Read more.
There is a clear research gap in understanding how future pathways and disruptions to the New Zealand (NZ) agricultural system will have an impact on the environment and productivity. Agriculture is in a period of significant change due to market disruptions, climate change, increasingly stringent environmental regulations, and emerging technologies. In NZ, agriculture is a key sector of the economy, therefore government and industry need to develop policies and strategies to respond to the risks and opportunities associated with these disruptors. To address this gap, there is a need to develop an assessment tool to explore pathways and interventions for increasing agricultural profitability, resilience, and sustainability over the next 5–30 years. A decision support tool was developed through Stella Architect, bringing together production, market values, land use, water use, energy, fertiliser consumption, and emissions from agricultural sectors (dairy, beef, sheep, cereals, horticulture, and forests). The parameters are customisable by the user for scenario building. Two future trend scenarios (Business as usual, Optimisation and technology) and two breakaway scenarios (Carbon farming, Reduction in dairy demand) were simulated and all met carbon emissions goals, but profitability differed. Future environmental regulations can be met by adjusting levers associated with technology, carbon offsets, and land use. The model supports the development and assessment of pathways to achieve NZ’s national agriculture goals and has the potential to be scaled globally. Full article
(This article belongs to the Special Issue New Perspective of Artificial Intelligence and Data Analytics on the Agricultural Land)
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25 pages, 11407 KiB  
Article
Comparative Analysis of Photosynthetically Active Radiation Models Based on Radiometric Attributes in Mainland Spain
by Ousmane Wane, Julián A. Ramírez Ceballos, Francisco Ferrera-Cobos, Ana A. Navarro, Rita X. Valenzuela and Luis F. Zarzalejo
Land 2022, 11(10), 1868; https://doi.org/10.3390/land11101868 - 21 Oct 2022
Cited by 2 | Viewed by 1608
Abstract
The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal [...] Read more.
The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) from their dimensionless indices atmospheric clearness index (kt), horizontal diffuse fraction (kd), and normal direct fraction (kb) together with solar elevation angle (α). GHI, DHI, and DNI data with 1-minute frequencies in the period from 2016 to 2021 from CEDER-CIEMAT, in a northern plateau, and PSA-CIEMAT in the southeast of the Iberian Peninsula, were used to compare two locations with very different climates according to the Köppen—Geiger classification. A total of 15 multilinear models were fitted and validated (with independent training and validation data) using first the whole dataset and then by kt intervals. In most cases, models including the clearness index showed better performance, and among them, models that also use the solar elevation angle as a variable obtained remarkable results. Additionally, according to the statistical validation, these models presented good results when they were compared with models in the bibliography. Finally, the model validation statistics indicate a better performance of the interval models than the complete models. Full article
(This article belongs to the Special Issue New Perspective of Artificial Intelligence and Data Analytics on the Agricultural Land)
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