The document discusses the importance of integrating soil carbon (C) information into land use decision-making for effective carbon sequestration and restoration goals. It emphasizes the need for decision-makers to access quick, specific, contextually relevant, and accurate data to enhance decision quality, as traditional measurement methods are often inadequate. Additionally, it advocates for a probabilistic approach to manage uncertainty in carbon estimates, enabling better decision support in real-time.

1. Considering soil C information in
decision-making
Eike Luedeling
World Agroforestry Centre & University of Bonn

2. Soil C sequestration goals
• Many C sequestration and restoration goals
have been set
• Much scientific discussion revolves around ways
to monitor progress towards set targets
• Plenty of open questions: appropriate baselines,
measurement errors, spatial and temporal
variability, …

3. Soil C sequestration goals
• Probably not much, because information on land
use effectiveness would come in much too late
• The time to make a change is the time of land use
decision making!
• How would resolving the monitoring problems
enhance C sequestration?

4. Elements of a good decision
Soil C sequestration goals
• Post-hoc evaluation doesn’t allow evaluating
decision quality
• It’s even less helpful for raising decision
quality, which is the goal of decision support
How can we provide ‘right information’ on
carbon for land use decisions?
• Right frame
• Right preferences
• Right alternatives
• Right information
• Right reasoning
• Committed decision-maker
• Right information

5. Supporting decisions in real-time
Decision-makers have many things to consider

6. Supporting decisions in real-time
Land use
decision
?
Soil C
sequestration
Food
production
Income
generation
Follow
traditions
Low production
cost
Low labor
input
Many objectives Trade-offs between objectives

7. Decision analysis
Luedeling and Shepherd, 2016. Solutions 7(5), 46-54 (slightly modified).

8. Very few decisions have large research budgets, especially for peripheral factors
Soil C information needs in decision-making
• To be useful during decision-making, soil C information must be:
Cheap
Decision-makers cannot wait for the results of long-term studiesQuick
Must be specific to the location and the decision option
Context-
specific
Must apply to expected future resultsProspective
Should not be wrong (easier than it may seem)Accurate

9. Making correct estimates
Soil C for decision option
Very rarely possible and not easily
distinguished from a wrong point estimate
Soil C for decision option
A perfect point estimate
Preferable to work with ranges, not
point estimates
Soil C for decision option

10. Making correct estimates
Soil C for decision option
Allows expressing our uncertainty
Soil C for decision option
Little uncertainty
The worst case – precise but
inaccurate
Soil C for decision option
Lots of uncertainty
But both are accurate! The actual value is contained within the ranges. Wrong estimate – actual value not
contained within range

11. Adjusting estimates by data availability
Expected soil C for a land use option
Data availability, research effort / budget
Highest achievable precision
(there’s always an unknowable
dimension)
For providing tailored decision-support, we need ways to
distill existing knowledge into best possible range estimates

12. Elements of such a mechanism
Literature
Collation Generalization
Holistic
‘brainstorming’
on decisions
Probabilistic
model
Relevant
factors
Expert ‘calibration’
C sequestration
projection
Important
processes
Other sources
What evidence
exists?
What is the
plausible
range?
Translation to
decision
context
What is the
plausible range
in this context?
Other
outcomes
e.g. soil C sequestration
For use in
coarse, high-
level models
For use in
decision-
specific models
Plausible ranges

13. Conclusions
• To really support decisions, we need a decision-oriented mindset
• Decision-makers need cheap, quick, context-specific, prospective
and accurate information
• For specific decisions, the usual measurement-based approach
can’t deliver this
• We need effective ways to capture what is (not) known and
translate it to new decision contexts
Thanks for your attention!
luedeling@uni-bonn.de

14. Probabilistic simulation
Normal
model
Precise numbers
as input
42
Precise number as
output
Probabilistic
model
Distributions as
input, because
precise values are
unknown
Distribution as
output
• Allows working with
variables that we don’t
have perfect knowledge on
• Requires characterizing
our uncertainty about
them
• Common methods are
Monte Carlo simulation
and Bayesian Network

15. Decision quality
• A good decision is characterized by
• A useful frame (purpose, perspective, and
scope of the decision problem)
• Feasible and diverse alternatives
• Meaningful and reliable information
• Clear values, preferences, and trade-offs
• Logically sound reasoning
• Commitment to action
http://dbceducation.com/wp-content/uploads/2015/12/d3c97e_74476856d6296d7b7a8607155b7584a2.jpg
High chance of meeting objectives