Daniel L. Sandars Natural Resources Management Centre,

School of Applied Science, Cranfield University

Contribution to:Foresight Land Use Futures and RELU’s workshop

on land valuation and decision making

22nd July 2009, Wallacespace, London.

Predicting farmer decision behaviour, taking a planning model beyond profit maximisation!

Options for biodiversity on lowland arable farms

Soils and Weather

Workable hours

Profitability (or loss)

Crop and livestock outputs

Environmental Impacts

Possible crops, yields, maturity

dates, sowing dates

Silsoe Whole Farm Model

Linear programme, important features timeliness penalties,

rotational penalties, workability per task,

uncertainty

Machines and

people

Constraints and

penalties

Voluntary conservation behaviour

• How would free conservation education influence farmer behaviour?

• What types of policy intervention do farmers find unacceptable?

Multi-criteria methods

Discrete choice problems Continuous choice problems

Methods Multi-criteria Decision Making, Analytic Hierarchy Process, Outranking methods, etc

Goal programming, Compromise programming, Multiple Objective programming

Features Elicits a rich picture of attributes. Formal problem structuring methods. Interactive with a few motivated decision makers

Simple view of attributes. Few examples of formal problem structuring methods. Examples of non-interactive uses

Role Mostly prescriptive solutions, but have seen AHP claim to predict the outcome of the US presidential election

Most examples prescriptive. Often cop-outs from a full economic cost profit maximisation!!

Weight distributionattributes (metrics)

0

0.05

0.1

0.15

0.2

0.25

0.3

0 2 4 6 8 10 12 14 16

Attribute

No

rmal

ised

wei

gh

t

centroid

observed

Lessons

• Data, models and knowledge the relate management action to outcomes are very limiting for non-monetary objectives.

• There are some serious challenges in representing highly abstract objectives, such as aesthetic attractiveness, with a set of quantifiable proxy attributes

• Computing power reduced the computational burdens of solving [well constructed] integer and non-linear models

Recommendations

• Beware anthropocentricity or extend the planning horizon

• Be adaptive and admit that valuation is only ever partial and with incomplete knowledge. Adopt a precautionary approach

• Beware of collapsing valuation to a single number when what is required is a varied and balanced profile of ecosystem services!

• Beware that wholes maybe more important than parts

• Consider efficiency based approaches DEA and related techniques