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The Potassium Cycle and Its Relationship to Recommendation Development

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dc.contributor.author Brouder, Sylvie M.
dc.contributor.author Volenec, Jeffrey J.
dc.contributor.author Murrell, Scott
dc.date.accessioned 2021-12-10T03:19:06Z
dc.date.available 2021-12-10T03:19:06Z
dc.date.issued 2021
dc.identifier.citation Brouder S.M., Volenec J.J., Murrell T.S. (2021) The Potassium Cycle and Its Relationship to Recommendation Development. In: Murrell T.S., Mikkelsen R.L., Sulewski G., Norton R., Thompson M.L. (eds) Improving Potassium Recommendations for Agricultural Crops. Springer, Cham. https://doi.org/10.1007/978-3-030-59197-7_1 en_US
dc.identifier.isbn 978-3-030-59197-7
dc.identifier.uri https://doi.org/10.1007/978-3-030-59197-7_1
dc.identifier.uri ${sadil.baseUrl}/handle/123456789/1656
dc.description 46 p. ; PDF en_US
dc.description.abstract Nutrient recommendation frameworks are underpinned by scientific understanding of how nutrients cycle within timespans relevant to management decision-making. A trusted potassium (K) recommendation is comprehensive enough in its components to represent important differences in biophysical and socioeconomic contexts but simple and transparent enough for logical, practical use. Here we examine a novel six soil-pool representation of the K cycle and explore the extent to which existing recommendation frameworks represent key plant, soil, input, and loss pools and the flux processes among these pools. Past limitations identified include inconsistent use of terminology, misperceptions of the universal importance and broad application of a single soil testing diagnostic, and insufficient correlation/calibration research to robustly characterize the probability and magnitude of crop response to fertilizer additions across agroecozones. Important opportunities to advance K fertility science range from developing a better understanding of the mode of action of diagnostics through use in multivariate field trials to the use of mechanistic models and systematic reviews to rigorously synthesize disparate field studies and identify knowledge gaps and/or novel targets for diagnostic development. Finally, advancing evidence-based K management requires better use of legacy and newly collected data and harnessing emerging data science tools and e-infrastructure to expand global collaborations and accelerate innovation. en_US
dc.language.iso en en_US
dc.publisher Springer Cham en_US
dc.title The Potassium Cycle and Its Relationship to Recommendation Development en_US
dc.type Book chapter en_US


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