Optimising precision agriculture choices for arable farmers in Germany and the UK: the LINKDAPA approachLatherow, T., Arnoult, M., Engel, T., Gralfs, R., Hrynevych, O., Karampoiki, M., Mahmood, S., Murdoch, A., Paraforos, D., Ranieri, E., Todman, L. ORCID: https://orcid.org/0000-0003-1232-294X, Tranter, R. ORCID: https://orcid.org/0000-0003-0702-6505 and Hammond, J. ORCID: https://orcid.org/0000-0002-6241-3551 (2024) Optimising precision agriculture choices for arable farmers in Germany and the UK: the LINKDAPA approach. EuroChoices. ISSN 1478-0917
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1111/1746-692X.12426 Abstract/SummaryFarmer adoption of so-called Precision Agriculture (PA) or ‘smart’ technologies in the arable sector has grown in the last few decades with a focus on Global Navigation Satellite Systems (GNSS) and variable rate technologies (VRT). This has led to increased generation of large volumes of data about fields and their crop yields which could be used to increase the environmental and financial performance for farmers. However, survey results show that cost and adaptability have been issues for many farmers in the UK and Germany that have held back such adoption. The LINKDAPA (LINKing multi-source Data for Adoption of Precision Agriculture) project's approach sought to minimise both concerns by creating a customisable web platform that incorporates both GNSS and VRT into one, easy to use, affordable option for farmers. The project developed an online cloud-based decision support tool which takes into account different fertiliser strategies based on novel algorithms using soil, historic yield and satellite data. Co-created by researchers, farmers and agricultural technology firms, the LINKDAPA approach offers both economical and easy to implement solutions for farm management to mitigate resource loss-ratios such as in fertiliser use, provide financial performance analyses, and multi-year graphical imagery for soil mapping.
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