Low-Cost, Open-Source, and Low-Power: But What to Do With the Data?

Horsburgh, Jeffery S.; Caraballo, Juan; Ramírez, Maurier; Aufdenkampe, A. K.; Arscott, David B.; Damiano, S. G.

doi:10.3389/feart.2019.00067

Cited by 32 publications

(31 citation statements)

References 27 publications

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“…It has been developed as a general information model to integrate both in situ sensors data, and ex-situ analyses results data, two situations that are common in the agriculture domain. It has also been successfully applied to four different disciplines (hydrology, rock geochemistry, soil geochemistry, and biogeochemistry) to meet field and laboratory data management needs (Hsu et al, 2017;Horsburgh et al, 2019). The example data model shows how some of the project attributes such as construction costs, distance of transfer and annual volume of water transferred (Shumilova et al, 2018) can be represented in the envisioned data model.…”

Section: Data and Information Modelingmentioning

confidence: 99%

Agricultural Hydroinformatics: A Blueprint for an Emerging Framework to Foster Water Management-Centric Sustainability Transitions in Farming Systems

Celicourt¹,

Rousseau²,

Camporese³

2020

Front. Water

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It is increasingly recognized that water scarcity, rather than a lack of arable land, will be the major constraint to increase agricultural production over the next few decades. Therefore, water represents a unique agricultural asset to drive agricultural sustainability. However, its planning, management and usage are often influenced by a mix of interdependent economic, engineering, social, hydrologic, environmental, and even political factors. Such a complex interdependency suggests that a sociotechnical approach to water resources management, a subject of the field of Hydroinformatics, represents a viable path forward to achieve sustainable agriculture. Thus, this paper presents an overview of the intersection between hydroinformatics and agriculture to introduce a new research field called agricultural hydroinformatics. In addition, it proposes a general conceptual framework taking into account the distinctive features associated with the sociotechnical dimension of hydroinformatics when applied in agriculture. The framework is designed to serve as a stepping-stone to achieve, not only integrated water resources management, but also agricultural sustainability transitions in general. Using examples from agricultural water development to horticultural and livestock farming, the paper highlights facets of the framework applicability as a new paradigm on data flows/sources consideration, and information and simulation models engineering as well as integration for a holistic approach to water resources management in agriculture. Finally, it discusses opportunities and challenges associated with the implementation of agricultural hydroinformatics and the development of new research areas needed to achieve the full potential of this emerging framework. These areas include, for example, sensor deployment and development, signal processing, information modeling and storage, artificial intelligence, and new kind of simulation model development approaches.

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Section: Data and Information Modelingmentioning

confidence: 99%

Agricultural Hydroinformatics: A Blueprint for an Emerging Framework to Foster Water Management-Centric Sustainability Transitions in Farming Systems

Celicourt¹,

Rousseau²,

Camporese³

2020

Front. Water

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“…Over the past several years, there has been a general reduction in prices of sensors and dataloggers; however, cost continues to be an important limitation for scientific research [16,17]. More recently, open-source electronics hardware, specifically Arduino, has been identified as a viable alternative for expensive, commercial instrumentation in scientific research [18].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters

Pacheco

Horsburgh

Tracy

2020

Sensors

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We present a low-cost (≈$150) monitoring system for collecting high temporal resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically driven, positive displacement, residential water meters and can collect data at a variable time resolution interval. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah, for a period of over 1 month. Battery life for the device was estimated to be over 5 weeks with continuous data collection at a 4 s time interval. Data collected using this system, under ideal installation conditions, was within 2% of the volume recorded by the register of the meter on which they were installed. Results from field deployments are presented to demonstrate the accuracy, functionality, and applicability of the system. Results indicate that the device is capable of collecting data at a temporal resolution sufficient for identifying individual water use events and analyzing water use at coarser temporal resolutions. This system is of special interest for water end use studies, future projections of residential water use, water infrastructure design, and for advancing our understanding of water use timing and behavior. The system’s hardware design and software are open source, are available for potential reuse, and can be customized for specific research needs.

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“…More importantly, it allows investigation of processes whose effects can only be identified over long periods of time and for revealing new questions which could not have been anticipated at the time the monitoring began (Burt, 1994). International programs such as LTER-Europe (Mollenhauer et al, 2018) and GLEON (Hanson et al, 2018), as well as the increasing use of remote sensing data (Scholefield et al, 2016;Rowland et al, 2017), sensor data (e.g., Evans et al, 2016;Horsburgh et al, 2019) and citizen science projects (e.g., Pescott et al, 2015;Brereton et al, 2018) has greatly increased the diversity of volume of environmental data and offers exciting new opportunities (Reis et al, 2015). It has become standard practice, and for some data centres and publications compulsory, to associate these datasets with helpful metadata and many of these would have passed some quality assurance/quality control (QA/QC) procedure prior to publication.…”

Section: Introductionmentioning

confidence: 99%

State Tagging for Improved Earth and Environmental Data Quality Assurance

Tso

Henrys

Rennie

et al. 2020

Front. Environ. Sci.

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Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term "state" here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established longterm monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily.

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Low-Cost, Open-Source, and Low-Power: But What to Do With the Data?

Cited by 32 publications

References 27 publications

Agricultural Hydroinformatics: A Blueprint for an Emerging Framework to Foster Water Management-Centric Sustainability Transitions in Farming Systems

Agricultural Hydroinformatics: A Blueprint for an Emerging Framework to Foster Water Management-Centric Sustainability Transitions in Farming Systems

A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters

State Tagging for Improved Earth and Environmental Data Quality Assurance

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