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Land
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Big Data Analysis for Sustainable Agriculture
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Big Data Analysis for Sustainable Agriculture

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (21 July 2023) | Viewed by 10480

Special Issue Editors

Dr. Daniel P Roberts

E-Mail Website
Guest Editor
USDA- Agricultural Research Service, Sustainable Agricultural Systems Laboratory (SASL), Partnerships for Data Innovations (PDI), Beltsville, MD 20705, USA
Interests: ecologically based disease control; sustainable agriculture; data innovations; knowledge management
Nicholas M. Short, Jr.

E-Mail Website
Guest Editor Assistant
Esri, Vienna, VA 22182, USA
Interests: applied geoinformatics; data science; knowledge management; AI; machine learning; NLP
Dr. Sunil Kumar

E-Mail Website
Guest Editor
USDA-Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Raleigh, NC 27606, USA
Interests: ecology; quantitative risk analysis; data science
Dr. Michael Buser

E-Mail Website
Guest Editor
USDA- Agricultural Research Service, Sustainable Agricultural Systems Laboratory (SASL), Partnerships for Data Innovations (PDI), Beltsville, MD 20705, USA
Interests: agricultural engineering; data Innovations; knowledge management

Special Issue Information

Dear Colleagues,

Population growth, climate change, and their impacts on food supply are driving the need for greater awareness and measurement of the agricultural sector as it relates to other ecosystems and economic sectors. Due to the ever-greater ability to generate data, data management solutions are quickly moving from the collection and storage of data to the analysis and packaging of analyses for consumption by a diverse community with varying skill sets, agendas, and roles. This Special Issue will include papers that focus on the causes of Big Data (i.e., traditional and non-traditional technologies), the ability to scale analysis due to advances in computing technology (i.e., the Cloud Platform), and the need for greater inclusion of diverse communities of practice based on technologies that foster collaboration.  We will attempt to answer questions about how to bring a traditionally fractured communities together to meet the grand challenges of agriculture that await due to increased population and environmental stress. 

Our goal is to both inform the LAND community about many of the advancements in agricultural computer science from a governmental, academic, and industrial point-of-view, and attract those in the computer science community who have not considered applying computer science to agriculture. We will solicit papers from the following topic areas.

  1. Big Data Generators

(1) The original Big Data generator: remote sensing from spaceborne/aerial platforms

(2) New data types (e.g., LIDAR, aerial oblique visual sensors)

(3) Field data generators from sensor networks, field mobile apps, and UAV/UGVs

(4) Data aggregation platforms

  1. Advanced Analytics that scale to handle “Big Data”

(1) Scalable analytics using the cloud or high-performance computing architecture

(2) AI and computer vision techniques at multiple spatial scales

(3) Spatial analytics & Geo AI

  1. Community Development through Ag Big Data Platforms

(1) Decision support tools using human-centered design

(2) Collaboration and knowledge management techniques, such as the use of ontologies, graph databases, recommender engines, etc.

(3) Multi-modal interfaces using NLP, speech recognition, robotic process automation, etc.

Dr. Daniel P Roberts
Nicholas M. Short, Jr.
Dr. Sunil Kumar
Dr. Michael Buser
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

  • big data
  • data analytics
  • Geo AI
  • remote sensing
  • sensor networks
  • community development
  • knowledge management
  • decision support tools
  • graph databases
  • IoT
  • computer vision

Published Papers (3 papers)

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Research

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17 pages, 1131 KiB  
Article
Leveraging Big Data to Preserve the Mississippi River Valley Alluvial Aquifer: A Blueprint for the National Center for Alluvial Aquifer Research
by Amanda M. Nelson, Nicolas E. Quintana Ashwell, Christopher D. Delhom and Drew M. Gholson
Land 2022, 11(11), 1925; https://doi.org/10.3390/land11111925 - 29 Oct 2022
Viewed by 1447
Abstract
The challenge of a depleting Mississippi River Valley Alluvial Aquifer (MRVAA) requires reducing groundwater withdrawal for irrigation, increasing aquifer recharge, and protecting water quality for sustainable water use. To meet the challenge, the National Center for Alluvial Aquifer Research (NCAAR) is oriented towards [...] Read more.
The challenge of a depleting Mississippi River Valley Alluvial Aquifer (MRVAA) requires reducing groundwater withdrawal for irrigation, increasing aquifer recharge, and protecting water quality for sustainable water use. To meet the challenge, the National Center for Alluvial Aquifer Research (NCAAR) is oriented towards producing scientific work aimed at improving irrigation methods and scheduling, employing alternative water sources, and improving crop management and field practices to increase water use efficiency across the region. Big data is key for NCAAR success. Its scientists use big data for research in the form of various soil, weather, geospatial, and water monitoring and management devices to collect agronomic or hydrogeologic data. They also produce, process, and analyze big data which are converted to scientific publications and farm management recommendations via technology transfer. Similarly, decision tools that would help producers leverage the wealth of data they generate from their operations will also be developed and made available to them. This article outlines some of the many ways big data is intertwined with NCAAR’s mission. Full article
(This article belongs to the Special Issue Big Data Analysis for Sustainable Agriculture)
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20 pages, 7620 KiB  
Article
Evaluation of Maximum Entropy (Maxent) Machine Learning Model to Assess Relationships between Climate and Corn Suitability
by Abigail Fitzgibbon, Dan Pisut and David Fleisher
Land 2022, 11(9), 1382; https://doi.org/10.3390/land11091382 - 23 Aug 2022
Cited by 14 | Viewed by 4816
Abstract
Given the impact that climate change is projected to have on agriculture, it is essential to understand the mechanisms and conditions that drive agricultural land suitability. However, existing literature does not provide sufficient guidance on the best modeling methodology to study crop suitability, [...] Read more.
Given the impact that climate change is projected to have on agriculture, it is essential to understand the mechanisms and conditions that drive agricultural land suitability. However, existing literature does not provide sufficient guidance on the best modeling methodology to study crop suitability, and there is even less research on how to evaluate the accuracy of such models. Further, studies have yet to demonstrate the use of the Maximum Entropy (Maxent) model in predicting presence and yield of large-scale field crops in the United States. In this study, we investigate the application of the Maxent model to predict crop suitability and present novel methods of evaluating its predictive ability. Maxent is a correlative machine learning model often used to predict cropland suitability. In this study, we used Maxent to model land suitability for corn production in the contiguous United States under current bioclimatic conditions. We developed methods for evaluating Maxent’s predictive ability through three comparisons: (i) classification of suitable land units and comparison of results with another similar species distribution model (Random Forest Classification), (ii) comparison of output response curves with existing literature on corn suitability thresholds, and (iii) with correlation of predicted suitability with observed extent and yield. We determined that Maxent was superior to Random Forest, especially in its modeling of areas in which land was likely suitable for corn but was not currently associated with observed corn presence. We also determined that Maxent’s predictions correlated strongly with observed yield statistics and were consistent with existing literature regarding the range of bioclimatic variable values associated with suitable production conditions for corn. We concluded that Maxent was an effective method for modeling current cropland suitability and could be applied to broader issues of agriculture–climate relationships. Full article
(This article belongs to the Special Issue Big Data Analysis for Sustainable Agriculture)
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Review

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19 pages, 1313 KiB  
Review
Scalable Knowledge Management to Meet Global 21st Century Challenges in Agriculture
by Nicholas M. Short, Jr., M. Jennifer Woodward-Greene, Michael D. Buser and Daniel P. Roberts
Land 2023, 12(3), 588; https://doi.org/10.3390/land12030588 - 28 Feb 2023
Cited by 1 | Viewed by 2975
Abstract
Achieving global food security requires better use of natural, genetic, and importantly, human resources—knowledge. Technology must be created, and existing and new technology and knowledge deployed, and adopted by farmers and others engaged in agriculture. This requires collaboration amongst many professional communities world-wide [...] Read more.
Achieving global food security requires better use of natural, genetic, and importantly, human resources—knowledge. Technology must be created, and existing and new technology and knowledge deployed, and adopted by farmers and others engaged in agriculture. This requires collaboration amongst many professional communities world-wide including farmers, agribusinesses, policymakers, and multi-disciplinary scientific groups. Each community having its own knowledge-associated terminology, techniques, and types of data, collectively forms a barrier to collaboration. Knowledge management (KM) approaches are being implemented to capture knowledge from all communities and make it interoperable and accessible as a “group memory” to create a multi-professional, multidisciplinary knowledge economy. As an example, we present KM efforts at the US Department of Agriculture. Information and Communications Technology (ICT) is being developed to capture tacit and explicit knowledge assets including Big Data and transform it into curated knowledge products available, with permissions, to the agricultural community. Communities of Practice (CoP) of scientists, farmers, and others are being developed at USDA and elsewhere to foster knowledge exchange. Marrying CoPs to ICT-leveraged aspects of KM will speed development and adoption of needed agricultural solutions. Ultimately needed is a network of KM networks so that knowledge stored anywhere can be used globally in real time. Full article
(This article belongs to the Special Issue Big Data Analysis for Sustainable Agriculture)
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