{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:50:14Z","timestamp":1760147414945,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Resource Management (NRM) Research Alliance"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Contemplation of potential strategies to adapt to a changing and variable climate in agricultural cropping areas depends on the availability of geo-information that is at a sufficient resolution, scale and temporal length to inform these decisions. We evaluated the efficacy of creating high-resolution, broad-scale indicators of yield from simple models that combine yield mapping data, a precision agriculture tool, with the normalised difference vegetation index (NDVI) from Landsat 5 and 7 ETM+ imagery. These models were then generalised to test its potential operationalisation across a large agricultural region (&gt;1\/2 million hectares) and the state of South Australia (&gt;8 million hectares). Annual models were the best predictors of yield across both areas. Moderate discrimination accuracy in the regional analysis meant that models could be extrapolated with reasonable spatial precision, whereas the accuracy across the state-wide analysis was poor. Generalisation of these models to further operationalise the methodology by removing the need for crop type discrimination and the continual access to annual yield data showed some benefit. The application of this approach with past and contemporary datasets can create a long-term archive that fills an information void, providing a powerful evidence base to inform current management decisions and future on-farm land use in cropping regions elsewhere.<\/jats:p>","DOI":"10.3390\/ijgi12020050","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T04:21:43Z","timestamp":1675398103000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region"],"prefix":"10.3390","volume":"12","author":[{"given":"Greg","family":"Lyle","sequence":"first","affiliation":[{"name":"School of Population Health, Curtin University, Bentley, Perth 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-6870","authenticated-orcid":false,"given":"Kenneth","family":"Clarke","sequence":"additional","affiliation":[{"name":"School of Agriculture, Food and Wine, The University of Adelaide, Adelaide 5005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-1729","authenticated-orcid":false,"given":"Adam","family":"Kilpatrick","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, The University of Adelaide, Adelaide 5005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1872-5267","authenticated-orcid":false,"given":"David McCulloch","family":"Summers","sequence":"additional","affiliation":[{"name":"UniSA Business, The University of South Australia, Adelaide 5001, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bertram","family":"Ostendorf","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, The University of Adelaide, Adelaide 5005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.enpol.2012.04.066","article-title":"Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability","volume":"47","author":"Erb","year":"2012","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1111\/gcb.12160","article-title":"How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?","volume":"19","author":"Smith","year":"2013","journal-title":"Glob. 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