<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/9F26ED35-FE90-4504-9B5C-6255981C3B75" ns1:id="9F26ED35-FE90-4504-9B5C-6255981C3B75"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/85EB3888-E411-4693-BD68-1D0421F2FB13" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/0B5F19C6-0F31-4A60-A6FF-3CC295A74D41" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/39AB165A-1227-4902-9CE2-2D693C0778EC" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EE51FD90-3802-472B-98BB-5C0046A6B598" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/6B57D64D-182B-4847-8A8F-366E0669D013" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/0B5F19C6-0F31-4A60-A6FF-3CC295A74D41" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/B2B860C0-CDD0-493C-A5F6-93A87E40B297" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2017-09-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/929700D3-9BF6-4CF7-9808-16582EAC425D" ns1:rel="FUND" ns1:start="2016-09-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">132348</ns2:identifier></ns2:identifiers><ns2:title>SHiFT: &amp;not;Sentinel-2 -compatible Historical datasets for Future crop Targeting</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>The project will investigate the use of archive satellite imagery to predict spatial variability within arable fields.

Many applications of precision agriculture use current satellite imagery to provide guidance on localised

management operations, for example application of Nitrogen fertiliser, but assumptions have to be made

about the causes of spatial variation. A 20-year archive of satellite image data will be developed to assess the

degree of persistence of spatial patterns over years and their dependence on weather and cropping factors.

Maps of potential yield variation and other interpretive tools will allow more intelligent, context-based

assessments that are expected to lead to improved land management with both economic and environmental

benefits. The project is being conducted by a consortium led by SOYL, together with ADAS, RSAC, AHDB and the

University of Nottingham.</ns2:abstractText></ns2:project>