About the Collaboration
The transition to more sustainable agriculture is driving a growing need for more and better data to enable people working across agriculture, environment and heritage sectors to take action to improve soil health, produce high quality food, increase biodiversity, reduce harmful impacts on ecosystems, and engage communities with the heritage and future of agricultural land. Remote and near-surface sensing data are essential to this work, used to assess the current state of agricultural land and monitor how it is changing over time. Today’s commercial precision agricultural data collection, analysis, exchange and reporting practices do not meet the needs of emerging models of sustainable agricultural land management, which are data-driven and operate across traditional divides between sectors.
This collaboration between University of Glasgow researchers and Agrivation Ltd, a precision agriculture service provider, produced a project scoping and planning toolkit designed to guide service providers and their clients through the process of acquiring, performing initial analysis, circulating, and reporting on remote and near-surface sensing data, taking into account the needs of cross-sector data users. Following feedback from workshops and document reviews, the toolkit is now in trail in commercial projects. By using the planning toolkit, Agrivation aims to promote the collection of data that will be suitable for use in diverse sustainable agriculture applications and raise awareness of its reuse potential.
“As a direct result of working with the ipaast project team, we are redeveloping the data collection methodology for my business. […] Together with my software partners (AgAnalyst) I will be developing new processes to support higher precision data collection […] and we aim to develop techniques to display the data, so it is fit for purpose in high resolution robotic operations in the future.”
Clive Blacker, Agrivation CEO
Project Outcomes
This project resulted in the creation of a Written Scheme of Investigation (WSI) template for use in the contracting and planning phase of precision agricultural surveys. This document template, adapted from a model provided by Magnitude Surveys Ltd, a social enterprise and independent provider of archaeological geophysics, is designed to guide precision agricultural service providers in the planning of projects which will produce data that is readily reusable in environmental and heritage management applications. Increasing the reuse potential of these data adds value to the service provided: farmers and landowners who are commissioning the survey gain the potential to redistribute the data or use it as evidence in reporting processes; agricultural service providers can provide coordinated surveys to multiple clients, lowering their per-project costs and expanding their potential client base; environmental and heritage agencies can reduce the costs of acquiring monitoring data by commissioning new data as part of a consortium with agricultural partners or by reusing existing data collected to documented standards.
Funding for this collaboration was provided by the AHRC IAA Impact Generation Fund at the University of Glasgow.
The Planning Toolkit:
Templates and Example Documents for service providers planning geophysical or related data collection compatible with agricultural, archaeological and environmental applications
Sample Data Archive (on zenodo)
Personal reflections from the Agrivation – Ipaast Project Collaboration
Clive Blacker
What have I learned from archaeology through collaboration with the Ipaast Project?
For someone not exposed to archaeological methodologies in the past, and someone open at least to sharing data openly, archaeological methodology has come across as very comprehensive and it’s been a learning curve to understand the levels of detail and document structures that are required. From the project start, from job planning of a potential survey to the transparency to which the survey is undertaken, the detail to which the data is captured and how this is shared with stakeholders before anything is done, is something that’s not seen to the same extent in agriculture. For example, written statements of investigation highlighting survey area(s), methodology, data capture, handling of data (including archiving), alongside health and safety are all accounted for in detail. Agricultural survey planning, in comparison, is generally arranged over a conversation with little data exchange or clear communication related to methodology applied at any stage and is typically signed off on a handshake and a basis of trust.
There are clear differences between the practices followed, which have, to their detriment, affected agricultural data trust, to a point where the industry is stuck discussing data ownership, protection and FAIR use. Farmers are often frustrated at the end of contracts with lack of access to data they have paid to collect. There have been instances of serious misunderstandings by farmers regarding the standards and ownership of these data. Archaeology is working towards the opposite, with an emphasis on open protocols, shared learnings, data-processing best practice shared, open data and shared archives; this is perhaps something agriculture can learn a lot from. A written scheme of investigation, a document commonly used in archaeology to set out how a survey will be conducted and to clarify matters of data ownership and plans for metadata, data analysis and dissemination, would never be discussed before any survey in agriculture. This practice should be adopted in agriculture. On reflection, without one, it’s no wonder there’s confusion related to outputs and misunderstanding about the data when it’s presented back to farmers, or when farmers receive raw data lacking information which can support transparency and openness. Perhaps agriculture has something to learn from this?
My experience of working with Ipaast archaeologists has also highlighted the need for greater transparency and a need for agricultural data standards, methodologies, and shared best practice, so farmers can make informed choices to the technology and data they purchase, invest in and deal with. This would improve the status of the precision agricultural sector and allow farmers to get more value from the precision agriculture investments they make. The openness demonstrated within the Ipaast project in sharing best practice has further highlighted improved methods of data processing with shared learnings from archaeological experiences which will go a long way to improving the collection of data, the processing of it and the delivery of this back to the end user. As a direct result of working with the Ipaast project team, we are redeveloping the data collection methodology for my business to include collection of metadata, so all data collected can be interrogated by others for future reuse in an open way. In parallel, thanks to learning new techniques from Ipaast project team members and professional practitioners I met through workshops, together with my software partners (AgAnalyst) I will be developing new processes to support higher precision data collection. This includes processes for collecting reference plots for data calibration to produce more robust readings and better data correlations. The new methodology for processing these data is in development, and we aim to develop techniques to display the data, so it is fit for purpose in high resolution robotic operations in the future.
Beyond developing better data collection protocols, there are challenges elsewhere that the agricultural industry needs to address as well. As farmers of the land, we consider ourselves caretakers for the next generation, with a duty to protect and look after these assets. However, as we have increased mechanised operations, we have perhaps lost sight of the unknown history that we may be custodians of, and as such, there are some farmers who fear that if their land is found to have a rich history, it could negatively impact their farming style and practice. There is often little reward for farmers for protecting the known archaeology and history of the ground in which they operate. Consequently, not knowing about the archaeology and history can seem easier when making management decisions. If equipment continues to get bigger, continued damage to soils, including archaeological soils, is likely to occur, and many farmers may continue to not want to know about the archaeology on their land. However, future uses of lightweight robots could have multiple benefits: significant impact on the resolution at which we operate leading to improved agricultural management, new datasets at resolutions which could well reveal a whole host of new insights into the history and archaeology of the land, and new insights into their impact on soil health and crop production going forward.