4. Arable
farming
• Why a decline? – reduced winter food resources
• Intensification leads large-scale homogenisation
in the landscape
• Herbicides lead to few weeds surviving to harvest
• High capacity machinery leads to timely harvest
and the swift removal of residues and stubble
• Increased winter sown cropping leads to less over
wintering stubbles
5. Policy
questions
• How would farmers react, in the long term, to change?
• Climatic
• Technical
• Financial
• Regulatory
• Social
• How does the cropping, environmental emissions and
biodiversity change?
• What would make a particular management action appealing to
farmers?
• For example, how will farmers respond to increasing prices of
biofuel crops. What will the unintended consequences be?
6. Model-based
farm-level
policy impact
analysis
• Linear Programming, such as Silsoe whole-FARM Model
(SFARMMOD), is well established at predicting the
optimizing behavioural response of farmers in response to
choice and change in prices, technology and regulations.
• Recently extensions include environmental pollution, such
as nitrate leaching as multiple objectives to be constrained
or minimised
• We extend this modelling approach to predict the impact
of biodiversity policy on farmers and the consequences of
farming on biodiversity
7. 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
10. Key tasks
Three main types of model extension are envisaged
1) Quantified measures of biodiversity, which could
include four mammal species, indicator bird species,
and weed species.
2) Field boundary features and the effects of spatial
geometry. These are habitats that support
biodiversity.
3) Incorporate sets of criteria to explain and predict the
decision behaviour of a population of land managers
11. Weeds,
birds and
mammals
• A wide varied of detailed ecological models
• Habitat association models of birds
• Difference equation and Markov chain models of weed
dynamics
• Game theory models of bird populations and winter feed
availability
• Development of a single metric ‘biodiversity units’?
• Fitting these to an LP requires meta-modelling to enable each
to be quantified for the set of all farm plans
13. boundary
features
Spatial
geometry
effects
• The length and depth of field boundary per cropped
hectare effects field shape which effects the
efficiency of field work
• A model of field work efficiency is being
developed to quantify the effects and determine
significant non-linear behaviour
• At a larger scale the increase of contract farming
operations can mean entire farms are in a single crop
in a given year
15. Decision
Making
Behaviour 1
• Profit maximising (long-term net farm profit) accounts well for
the aggregate production behaviour of farmers, but what about
conservation behaviour?
• At farm level decision making behaviour may differ due
personal values, views on future prices, risk, and the
information available
• Conservation behaviour may involve the understanding of
objectives such as ‘stewardship of the land’, and ‘professional
pride/identity’, etc
• Aggregate behaviour can be built up from a distribution of
farmer values. Is this a better decision model?
16. Decision
Making
Behaviour 2
• Multiple Objective Decision Making (MODM) can be
used. It is based on Multiple Attribute Value Theory
(MAVT)
• The two common implementations are
• Goal Programming (GP): Objectives are satisfied
by obtaining a series of hierarchical goals
• Multiple Objective Programming (MOP):
Objectives are involved in a weighted trade-off
• Which is better …both or ANP or Stated Choice or…?
23. Summary
• Farmers on lighter and dryer soils can increase the
amount of stubble available more readily than those
on heavier wetter soils.
• However, in doing so the risks rise sharply
• Promoting spring crops does not in itself provide
more stubble.
• Raise farm incomes do to higher prices tends to
reduce winter stubble availability because the
benefits of timeliness progressively outweigh
machinery costs
25. Discussion
• Can we maintain linearity and its high utility
• Can we identify the ‘missing’ attributes? Do they
exist? Would we be better quantifying the farmers
true full economic costs?
• Can we quantify and model them for all farm plans?
• Can we elicit preferences and value functions?
• Can we generalise for all farmers for some farmers?
• Can readily evaluate future, as yet unspecified
choices by estimating their attributes only?
