Special Issue "Spatial Analysis of Agricultural Data"
Deadline for manuscript submissions: 10 December 2021.
Interests: spatial statistics; bayesian statistics; environmental statistics; biostatistics; epidemiology
Special Issues and Collections in MDPI journals
In the 21st century, the global population is expected to grow to 10 billion. The question of how to increase agricultural production to achieve food security and feed a growing population is one of the greatest challenges facing humanity. This needs to be addressed while maintaining sustainable agricultural systems and simultaneously facing challenges related to climate, resources and weather events. Automation with new technologies, sensors, yield monitors, internet of things (IoT) and drones and robots, as well as the use of GIS methods, artificial intelligence (AI), highly structured mathematical models and Big Data statistical techniques, serves as the basis for a global “Digital Twin”. This conceptualization will contribute to the development of site-specific conservation and management practices that will increase the income and global sustainability of agricultural systems. The spatial analysis of agricultural data is a key element in this context.
Satellite and aerial images, sensors and yield monitors provide information about production variability at macro and micro scales, with a great amount of agricultural data to be processed, represented, modeled and understood. Spatiotemporal models seem to offer additional benefits beyond the classical, spatially explicit modeling. Hierarchical models can deal with complex interactions by specifying parameters that change on several levels via the introduction of random effects.
The spread of transboundary plant pests and diseases caused by fungi, bacteria or viruses has increased significantly in recent years. These threats are causing significant losses and impacting food security. In essence, they spread by human-migrated movement and are windborne or vector-borne. A wide range of environmental, climatic and socioeconomic factors underlie their spatial patterns. In addition, factors such as changes in climate, habits or land use intervene and complicate the understanding of these processes.
This Special Issue is intended for a wide and multidisciplinary audience and presents some of the most recent advances and novel approaches in the spatial analysis of agricultural data.
Prof. Dr. Antonio López-Quílez
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. Applied Sciences 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 2000 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.
- precision agriculture
- ICT applications
- Internet of Things (IoT)
- GIS applications
- remote sensing
- spatial statistics
- geospatial artificial intelligence
- spatial prediction