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DSC4.5.2 - Foreseeable breakthroughs in soil science
Three critical failures of soil science
and opportunities to overcome...
Soil science has largely failed to meet
user needs due to three critical failures
Unreliable inference
• Rarely adequately sample geographic
area of interest for which
recommendations are to be made
Targe...
Ignore uncertainty
• When making & presenting
recommendations
• Source of data cannot be traced
• Recommendations rarely v...
No economics
• Rarely provide type and
form of soils information
required for economic
decision making
Farmer:
Short and l...
Reliable inference
• Define the region of interest
• Use statistical sampling frames
• Deploy low cost, rapid,
reproducibl...
Represent & communicate uncertainty
• Use distributions not averages
• Communicate uncertainty to users
• Maintain links t...
Integrate soil in economic decision making
• Define the decision
• Quantify benefits, costs, risks
• Translate into moneta...
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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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