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

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. Soil science has largely failed to meet
user needs due to three critical failures

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. 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. 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. 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. 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. 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