Online Farm Research Articles

WITHDRAWN: A smart agriculture information system delivering research data for adoption by the Australian grains industry

Online Farm Trials (OFT) is a bespoke online information system providing current and historical grains trials research data and information in Australia. It represents a technology that supports smart agriculture by enabling discoveries in the data. Its impact on users has been established, supporting industry knowledge and decision making and leading to improved work practices. The aim of this research is to identify the contribution of OFT, as a tool that supports smart agriculture, and to explore current adoption, barriers and the opportunities for expansion of the system in the Australian grains industry, as perceived by industry experts. In-depth, semi-structured interviews were conducted with leaders and innovators from the agriculture industry (N = 16). Thematic analysis of the qualitative data reveals widespread value of OFT as an important resource for accessing industry-dedicated, current and historical research trials data. Barriers to adoption were identified which included incomplete data, and a lack of industry-wide awareness. Opportunities to consolidate and expand the data, together with improved industry awareness, are essential to improving industry-wide adoption. Key learnings were identified in the research that has benefit for the industry, and consideration for the adoption of technology in smart agriculture. This includes functionality, audience, completeness of information/data, consideration of new technologies and the promotion of the system supported by real world examples to encourage industry adoption. Recommendations to support the design and development of digital platforms in agriculture, in the future, are also offered.

The O2 software framework and GPU usage in ALICE online and offline reconstruction in Run 3

The O² software framework and GPU usage in ALICE online and offline reconstruction in Run 3

Using an open source and resilient technology framework to generate and execute prescription maps for site-specific manure application

The development of precision farming solutions and the required network infrastructure appear to be progressing at different speeds and levels of deployment. In most of the world's rural areas, where farming takes place, network coverage and infrastructure is at a basic level. However, where digital farming solutions are applied, internet-dependent applications need backup solutions to enable the same or a comparable performance towards maintaining the functionality of the specific practice and ultimately food production. The aim is to avoid regressing to basic practices waiving advantages of established precision farming technologies. Based on a conceptual framework, defined by the authors in a previous work, site-specific slurry application was chosen as a concrete use case to implement and demonstrate fallback options to maintain efficient and reliable application even during internet outages. Initially, the differences between conventional and resilient IT infrastructure were contrasted and investigated. The generation of a prescription map (PM) based on the fusion of multiple parameters, clustered in the domains of soil, yield, and remote sensing, was performed utilizing open-source and offline usable software. For seamless transmission of the generated PMs, a new and innovative hardware platform was integrated into the machinery fleet and working process of a machinery ring (MR), which was acting as a service contractor in the inter-farm slurry application. Interoperability was considered by enabling the executing software to accept PMs of altering origin. The on- and off-line generation of PMs including data fusion, were also compared. Here the multi-parametric approach corresponded to the ground truth data, whereas the PMs generated in an online farm management information system (FMIS), using only one data source, showed deviations in management zone patterns and relative dose rates. In addition, the impact of the lack of online accessible data on the delineation of management zones was investigated and resulted in a switch from the medium to the high dose rate (DR) level in 9.1% of the field's area.

Use of digital technology for research data and information transfer within the Australian grains sector: A case study using Online Farm Trials

CONTEXTAgriculture is experiencing rapid change with the widespread availability of industry-specific technological and digital innovations. One example of this is Online Farm Trials (OFT), a user-facing web portal that systematises on-farm and field-based cropping research trial data for Australia's grains industry. The portal delivers access to research data, including legacy data, from thousands of grains trials projects that have been supplied by industry contributors. The portal is well established, having been informed by regular stakeholder input that has guided and informed the continued improvement of the portal from its development and implementation to continued operations. OBJECTIVEResearch was conducted across three time-points to assess the usage and application of OFT, and to examine its perceived impact on users to facilitate access to information supporting on-farm decision making and practice change within the Australian grains industry. METHODSQuantitative and qualitative data (portal usage and website analytics, surveys, in-depth interviews) were collected at three time points over 6 years with the aim of examining the usage and application of the portal. Portal users, data contributors, and other stakeholders from the grains and agriculture industries participated in this research. RESULTS AND CONCLUSIONSOver the three time points, a total of 89 surveys were completed and 49 interviews were conducted. Portal usage data confirms consistency in the number of visitors over time; most users of the portal were from Australia, with many accessing the portal on multiple occasions. Survey and interview data demonstrate that OFT is valued, widely used, and that the data on the portal are being broadly applied. Access to information and data through the portal (including legacy data) is used to support knowledge and to make sector-relevant decisions, and is assisting portal users in their workplace and work practices. The availability of data and information through the portal is increasing connections between industry and stakeholders across the grains sector. However, the trust and quality of contributor data has been consistently raised as a point of discussion by some portal users. SIGNIFICANCEThis research demonstrates the contribution that this data portal has on usage, adoption and application within the Australian grains industry. The insights and learnings about the application of digital technology for data and information access for the grains sector may be applicable to other agricultural sectors.

CREATIVE SOLUTIONS FOR AGRICULTURAL PRODUCTS PROMOTION

The article argues the relevance and feasibility of using marketing tools to promote agricultural products. The peculiarities of the promotion complex of agro-industrial products related to the specifics of the agricultural market are determined. Thus, it was found that the choice of means and methods of promoting agricultural products is determined by specific factors that depend on the type of market in which the company operates (B2C and B2B), agriculture (agriculture, manufacturing, processing, infrastructure), goods life cycle stages, modern promotion trends used by leading agricultural companies.
 The modern tendencies in the field of promotion of agricultural products, problems of application of creative in the process of promotion of agro - industrial products are resulted. The method of creativity SCAMPER (Substitute. Combine. Adapt. Modify. Put. Eliminate. Reverse), expediency of its application in the agricultural sphere is considered. Situations that do not allow to use a creative approach in finding solutions to promote agricultural products are analyzed.
 The efficiency of using Internet resources to promote agricultural products is argued. The main advantage of digitalization is the scaling of agribusiness. For large B2B companies, this is a way to enter foreign markets, to establish work for export. For small and medium-sized businesses, this is an increase in brand awareness, a way to expand the market, to attract new customers.
 The main types of available Internet resources for agribusiness are identified, including: free bulletin boards and forums on agricultural topics; online farm shops or aggregator sites that bring together producers and buyers; agricultural portals.
 Features of traditional channels of Internet promotion in the agricultural sphere are considered. The essence of content marketing as a tool of creative approach to product promotion is revealed. It is determined that the development and presentation of interesting content in the agricultural market helps to attract the attention of various target audiences - from farmers and investors to shareholders of the agricultural holding.
 A set of basic modern tools for creating successful content of agricultural companies has storytelling, videos, live broadcasts on social networks, webinars, 3D modeling, exhibitions in online format, sponsorship, participation in social projects, blogging. Examples of creative presentation of agricultural products by creating content are given. An analysis of the content of the surveyed companies suggests that with the help of well-developed, creative content there is a close emotional connection between the company and the consumer, strengthens trust in the brand, builds loyalty and increases sales.

Study on Online Farm Related Goods Trading

Study on Online Farm Related Goods Trading

Using agricultural metadata: a novel investigation of trends in sowing date in on-farm research trials using the Online Farm Trials database

Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry.

Using agricultural metadata: a novel investigation of trends in sowing date in on-farm research trials using the Online Farm Trials database

Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry.

The Australian digital Online Farm Trials database increases the quality of systematic reviews and meta-analyses in grains crop research

Synthesis and analysis of past cropping research can provide valuable information to direct future decisions around crop management. Systematic reviews and meta-analyses are considered gold standards in the synthesis and analysis of scientific research because they distil large amounts of information about complex issues, provide a summary of knowledge to date, and identify knowledge gaps. However, several issues concerning the methodologies employed to conduct systematic reviews have been identified; among them is the risk of publication bias when a review relies too heavily on ‘white’ literature from published academic sources and in so doing fails identify relevant ‘grey’ literature. Grey literature is inherently difficult to identify and collect, but forms a large portion of information available in many fields including agricultural-based research within Australia. The Online Farm Trials (OFT) database is a digital database of crop research field trial data from across Australia that has the potential for use as a discipline-specific source of grey literature to inform systematic reviews and meta-analyses. Using a case study approach to investigate the amount of information available on time of sowing (sowing date) on crop yield across Australia, we demonstrate that the OFT database provides easy access to transparent and reproducible search results similar to other commonly used academic databases.

Using agricultural metadata: a novel investigation of trends in sowing date in on-farm research trials using the Online Farm Trials database

Background: A growing ability and interest in the collection of data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from over thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other metadata elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry.

Design and Performance of Data Acquisition and Control System for the Muon g-2 Laser Calibration

The Muon g-2 Experiment at Fermilab (E989) will measure the muon magnetic anomaly with unprecedented precision (0.14 ppm), which yields a factor of 4 improvement with respect to the previous measurements at Brookhaven National Laboratory (BNL) (E821). To achieve this goal, the relative response of each calorimeter channel must be calibrated and monitored at a level better than $10^{-3}$ in the time window of the muon fill. The calibration system uses a laser source and photodetectors. The data acquisition (DAQ) of the system is designed around two field-programmable gate array (FPGA)-based boards and a custom crate bus. The front-end board manages the photodetector operation and signal processing and performs a first-level data concentration task. Up to 12 FPGA boards can be housed in a 6U crate. A readout master controls the boards, implements event-building functionalities, manages the monitoring interface, and facilitates calibration and debugging tasks. A gigabit-ethernet interface is used to transfer data to the on-line farm for storage and further processing. Presently, the system is working at Fermi National Accelerator Laboratory (FNAL). In this article, we present the DAQ system design, run control user interface, and system evaluation.

GPU-based reconstruction and data compression at ALICE during LHC Run 3

In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read out of minimum bias Pb-Pb collisions. The reconstruction strategy of the online offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration, and a posterior calibrated asynchronous reconstruction stage. The significant increase in the data rate poses challenges for online and offline reconstruction as well as for data compression. Compared to Run 2, the online farm must process 50 times more events per second and achieve a higher data compression factor. ALICE will rely on GPUs to perform real time processing and data compression of the Time Projection Chamber (TPC) detector in real time, the biggest contributor to the data rate. With GPUs available in the online farm, we are evaluating their usage also for the full tracking chain during the asynchronous reconstruction for the silicon Inner Tracking System (ITS) and Transition Radiation Detector (TRD). The software is written in a generic way, such that it can also run on processors on the WLCG with the same reconstruction output. We give an overview of the status and the current performance of the reconstruction and the data compression implementations on the GPU for the TPC and for the global reconstruction.

Designing a New Infrastructure for ATLAS Online Web Services

Within the ATLAS detector, the Trigger and Data Acquisition system is responsible for the online processing of data streamed from the detector during collisions at the Large Hadron Collider (LHC) at CERN. The online farm is composed of ~4000 servers processing the data read out from ~100 million detector channels through multiple trigger levels. The capability to monitor the ongoing data taking and all the involved applications is essential to debug and intervene promptly to ensure efficient data taking. The base of the current web service architecture was designed a few years ago, at the beginning of the ATLAS operation (Run 1). It was intended to serve primarily static content from a Network-attached Storage, and privileging strict security, using separate web servers for internal (ATLAS Technical and Control Network - ATCN) and external (CERN General Purpose Network and public internet) access. During these years, it has become necessary to add to the static content an increasing number of dynamic web-based User Interfaces, as they provided new functionalities and replaced legacy desktop UIs. These are typically served by applications on VMs inside ATCN and made accessible externally via chained reverse HTTP proxies. As the trend towards Web UIs continues, the current design has shown its limits, and its increasing complexity became an issue for maintenance and growth. It is, therefore, necessary to review the overall web services architecture for ATLAS, taking into account the current and future needs of the upcoming LHC Run 3. In this paper, we present our investigation and roadmap to re-design the web services system to better operate and monitor the ATLAS detector, while maintaining the security of critical services, such as Detector Control System, and maintaining the separation of remote monitoring and on-site control according to ATLAS policies.

Integrated automation for configuration management and operations in the ATLAS online computing farm

The online farm of the ATLAS experiment at the LHC, consisting of nearly 4000 PCs with various characteristics, provides configuration and control of the detector and performs the collection, processing, selection, and conveyance of event data from the front-end electronics to mass storage. Different aspects of the farm management are already accessible via several tools. The status and health of each node are monitored by a system based on Icinga 2 and Ganglia. PuppetDB gathers centrally all the status information from Puppet, the configuration management tool used to ensure configuration consistency of every node. The in-house Configuration Database (ConfDB) controls DHCP and PXE, while also integrating external information sources. In these proceedings we present our roadmap for integrating these and other data sources and systems, and building a higher level of abstraction on top of this foundation. An automation and orchestration tool will be able to use these systems and replace lengthy manual procedures, some of which also require interactions with other systems and teams, e.g. for the repair of a faulty node. Finally, an inventory and tracking system will complement the available data sources, keep track of node history, and improve the evaluation of long-term lifecycle management and purchase strategies.

Muon g-2 calibration system data flow

In the Muon g-2 Experiment at Fermilab, a calibration apparatus based on a set of laser sources and a distribution system has been designed and implemented by the INFN group. The light pulses are read by specific photo-detectors, whose signals are digitized by custom electronics modules designed to match the experimental requirements. The data frames of each module are transmitted to a controller board that performs the event-building process and transfers the reconstructed data to the online farm. In this work we present the architecture and data flow of the acquisition system that depends on the laser calibration program defined inside the Laser Control board. Experimental results on the overall system performances, also including the software processes running both at controller and farm level will be described. • A custom DAQ system performs a trigger-driven data collection. • Each crate hosts a master board and several slaves based on FPGA elements. • The crate event building is fully realized in hardware. • Monitoring facility to track in realtime data transfers and processing activities.

The Readout Controller for the Calibration System of the Muon g-2 Experiment

The Muon g-2 experiment at Fermilab will measure the muon anomalous magnetic moment $a=(g-2)/2$ to an unprecedented precision of 0.14 parts per million (ppm). To this aim, a calibration system made by a laser source and light distribution will provide short light pulses directly into each crystal of the 24 calorimeters to measure energy and arrival time of the decay positrons. Each calorimeter is composed of a $6\times 9$ matrix of $PbF_{2}$ crystals where each crystal is read by a silicon photomultiplier. Continuous monitoring and state-of-the-art calibration are required in order to control the detector response. The calibration light pulses are monitored, both at the laser output (source monitor) and at the end of the distribution system (local monitor), before delivery to the calorimeters. For example, the light pulses are read by specific photodetectors, whose signals are digitized by electronics designed to match the experimental requirements. All readout electronics boards are hosted in a crate where the controller manages the complete data collection, operates as an event builder and transfers data to the online farm system through a gigabit Ethernet connection. This data acquisition (DAQ) system is designed around a custom protocol and hardware to achieve high data transfer rate and event-building capability without software overhead. In this paper, after a general outline of this DAQ system, we describe in detail the main features of controller.

Why would we believe them? Meat consumers’ reactions to online farm animal welfare activism in Australia

ABSTRACTThe use of social media by animal activist organisations is of interest to those in the livestock production industries because of the perceived increased use and effectiveness of this medium for influencing consumers. Using qualitative data derived from focus groups and interviews, we explore how Australian meat consumers interact with animal welfare activism content posted to social media, either by activist organisations or members of the participants’ networks. Results indicate that meat consumers dismiss online animal welfare activism due to a perceived lack of credibility and being associated with a vegetarian or vegan ‘agenda’. Activists also were considered by participants to be ‘ignorant’, with participants suggesting they needed to experience animal farming first hand. Online activism was described as ‘slacktivism’ by our research participants, who felt that sharing something online does not create actual change in the real world and hence is not an authentic or meaningful form of activism. While farm animal welfare is of increasing concern to Australian consumers, this research suggests that information generated by activist organisations and shared via social media is unlikely to change meat eaters’ perceptions, at least in the current form in which it is being provided.

ATLAS TDAQ System Administration: Master of Puppets

Within the ATLAS detector, the Trigger and Data Acquisition system is responsible for the online processing of data streamed from the detector during collisions at the Large Hadron Collider at CERN. The online farm is comprised of ∼4000 servers processing the data read out from ∼100 million detector channels through multiple trigger levels. The configurtion of these servers is not an easy task, especially since the detector itself is made up of multiple different sub-detectors, each with their own particular requirements. The previous method of configuring these servers, using Quattor and a hierarchical scripts system was cumbersome and restrictive. A better, unified system was therefore required to simplify the tasks of the TDAQ Systems Administrators, for both the local and net-booted systems, and to be able to fulfil the requirements of TDAQ, Detector Control Systems and the sub-detectors groups. Various configuration management systems were evaluated, though in the end, Puppet was chosen as the application of choice and was the first such implementation at CERN.

Integrated monitoring of the ATLAS online computing farm

The online farm of the ATLAS experiment at the LHC, consisting of nearly 4100 PCs with various characteristics, provides configuration and control of the detector and performs the collection, processing, selection and conveyance of event data from the front-end electronics to mass storage.The status and health of every host must be constantly monitored to ensure the correct and reliable operation of the whole online system. This is the first line of defense, which should not only promptly provide alerts in case of failure but, whenever possible, warn of impending issues.The monitoring system should be able to check up to 100000 health parameters and provide alerts on a selected subset.In this paper we present the implementation and validation of our new monitoring and alerting system based on Icinga 2 and Ganglia. We describe how the load distribution and high availability features of Icinga 2 allowed us to have a centralised but scalable system, with a configuration model that allows full flexibility while still guaranteeing complete farm coverage. Finally, we cover the integration of Icinga 2 with Ganglia and other data sources, such as SNMP for system information and IPMI for hardware health.

Development of Farm simulation application, an example for gamification in higher education

The online applications and game are related to continuous innovation, technical and technological advances in last decades. Our digital society based on the technological advancements, because the changing consumer requirements needs a quick and new solutions in the field of technology. We created an online farm simulation application named Crownking. The program is based on PHP language and has a MySQL database. The game has a huge similarity to real life because it have factors which can directly affect the growth of the crops, such as the weather, soil quality, soil maintain and the player’s profession. Our main goal is help the students in learning with the help of gamification, and moreover to introduce the agriculture to the young generation, and give some spirit about farmer's life.