EURO 2007, Prague Monday 9 th July 2007

1. Biodiversity and Agricultural Production Planning by LPDaniel L. Sandars & E. Audsley EURO 2007, PragueMonday 9th July 2007

2. Structure • Background • Methodological challenges • Results • Summary & (Discussion)

3. Declining farmland birds A political objective to halt the decline

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 impactanalysis • Linear Programming, such as Silsoe whole-FARMModel (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 Machines and people Workable hours Possible crops, yields, maturity dates, sowing dates Constraints and penalties Silsoe Whole Farm ModelLinear programme, important features timeliness penalties, rotational penalties, workability per task, uncertainty Profitability (or loss) Crop and livestock outputs Environmental Impacts

8. Light Medium Heavy Workable hours - typical profile

9. Structure • Background • Methodological challenges • Results • Summary & (Discussion)

10. Key tasks Three main types of model extension are envisaged • Quantified measures of biodiversity, which could include four mammal species, indicator bird species, and weed species. • Field boundary features and the effects of spatial geometry. These are habitats that support biodiversity. • 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

12. LP model of weeds, etc

13. Field boundary featuresSpatial 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

14. Non linearity! Can we maintain linearity and model the effects of promoting an increase in hedges and probable reduction in field size

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…?

17. Structure • Background • Methodological challenges • Results • Summary & (Discussion)

18. Comparison of cropping

19. Sensitivity to commodity prices Prices £/t: W Wheat £78, S Wheat £81, Barley £73, Peas £87, Rape seed £150 Sandy clay loam with 595 mm annual rainfall

20. Promoting spring crops v. stubbles

21. Suppose over wintering stubbles are one measure of ‘stewardship’

22. Structure • Background • Methodological challenges • Results • Summary & (Discussion)

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

24. The ENDCollaborators

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?