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Three critical failures of soil science and opportunities to overcome them

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Soil science has largely failed to meet user needs due to these critical failures: spatial inference, uncertainty, economics

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Three critical failures of soil science and opportunities to overcome them

  1. 1. DSC4.5.2 - Foreseeable breakthroughs in soil science Three critical failures of soil science and opportunities to overcome them: spatial inference, uncertainty, economics Keith Shepherd
  2. 2. Soil science has largely failed to meet user needs due to three critical failures
  3. 3. Unreliable inference • Rarely adequately sample geographic area of interest for which recommendations are to be made Target area, convenience locations, few locations, what do sites represent ➢ Prevents valid inference of results for the region of interest
  4. 4. Ignore uncertainty • When making & presenting recommendations • Source of data cannot be traced • Recommendations rarely validated ➢Passes the risk on to the user ➢ Impedes learning on how to improve recommendations https://www.bfdc.com.au/interrogator/frontpage.v
  5. 5. No economics • Rarely provide type and form of soils information required for economic decision making Farmer: Short and long term returns and risks from adopting no tillage National policy: What is the value of ecosystem service benefits of a policy to maintain 1.5% soil organic carbon Costs and benefits of farm subsidy schemes Value of a national soil monitoring system The soils scientists’ lament!
  6. 6. Reliable inference • Define the region of interest • Use statistical sampling frames • Deploy low cost, rapid, reproducible measurement methods Shepherd et al. (2015). Land health surveillance and response: A framework for evidence-informed land management. Agricultural Systems 132: 93–106 Africa Soil Information Service EthioSIS, GhaSIS, NiSIS, TanSIS
  7. 7. Represent & communicate uncertainty • Use distributions not averages • Communicate uncertainty to users • Maintain links to original data • Validate recommendations • Focus further measurement on areas of uncertainty that matter Probability management systems Savage (2012). The flaw of averages.
  8. 8. Integrate soil in economic decision making • Define the decision • Quantify benefits, costs, risks • Translate into monetary terms • Use expert knowledge and data • Bayesian Networks or Monte Carlo simulation • Value of information analysis to drive further data needs • Luedeling E and Shepherd KD. 2016. Decision-Focused Agricultural Research. The Solutions Journal 7: 46-54. • Shepherd KD. How soil scientists can do a better job of making their research useful. The Conversation (Science & Technology) 14 August 2018. Effectiveness of erosion control Average sediment yield Cash flow over time Critical variables

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