1. A Spatial Analysis of the Beef Supply Response in Scotland Cesar Revoredo-Giha Montserrat Costa-Font Philip Leat SRUC-Food Marketing Research 150th EAAE Seminar “The Spatial Dimension in Analysing the Linkages between Agriculture, Rural Development and the Environment” Edinburgh, Scotland 22-23 October 2015

2. 22 Outline 1.Motivation 2.Purpose 3.State of the art 4.Theoretical model 5.Data 6.Method 7.Results and discussion

3. 33 Motivation Cattle production represents just over a quarter of total Scottish agricultural output. In June 2013: 447,000 beef cows (73%) and 166,000 dairy (27%). (Scottish Government, 2014) During the last 14 years an average reduction of over 4,600 cows per year and 14% total decline across the period has occurred. In 2013 411,719 beef cattle were slaughtered in Scotland from which 50.2% are steers, 41.1% heifers and 8.7% young bulls. NE (34%) and SW (48%) are the main regions: 2013 %SteersHeifersYoung bulls NW1.372.950.62 NE36.2336.5618.45 SW48.5547.2843.85 SE13.8513.2137.08

4. 44 Purpose Slaughtering levels are influenced by: production, disease, policy and market issues. The importance of prices as a way to encourage the supply of cattle to be finished is reflected is one of the recommendations of Scottish Beef 2020 Report (Scottish Government, 2014) However, there is no information about the Scottish supply response to changes in prices. This paper is an exploratory study aiming at estimate the supply elasticity to prices paid for cattle for four regions: North and South West and East of Scotland as well as for Scotland as an aggregate. A theoretical model and an empirical approach have been developed.

5. 55 State of the art Models on beef supply response for different countries and considering different variables reported negative and positive relations between beef slaughter and beef price, for both heifers and steers. Being negative more common in the short run and positive in the long run. Short run supply response explanations are mainly due to farmers’ investments behaviour and price expectations. Short runLong run Reutlinger (1966) ++ Jarvis (1974) -+ Rosen (1987) -+ Gordon (1990) -+ Marsh (1994) -+ Rosen et al. (1994) -+ Sarmiento and Allen (2000) -+ Aadland et al. (2000) Mixed evidences+ Aadland and Bailey (2001) ++

6. 66 State of the art Few works on the beef supply response to price for the UK have been done: Brookfield (1991):“the decision to sell is determined by farm capacity and the ability of the farmer to feed his stock at price which allow scope for profits to be made from the sale of retained animals in a fattened condition” (p. 11). Jones (1965): the proportion of calves reared tends to increase if the beef and milk prices increased.

7. 77 Structure of the theoretical model This is a model of a finisher, who buys in period t and has to sell either in period t+1 or period t+2

8. 88 Formulation of the theoretical model Farmer will decide to sell the animals in period t+2 (i.e., to fatten one period more) only if he expects to higher profits from it

9. 99 Formulation of the theoretical model A farmer will sell the cattle that is fattening in period t+2 only if it is profitable to carry them to the next period. The profits at time t+1 and t+2 are: Where, is the price per kilogram of the deadweight cattle in period t, is the weight of the animal at time t when the given feedingstuff is, is the price of the feed, the price paid for the animal to be fattened and is a discount factor

10. 10 Formulation of the theoretical model The first term of expression is negative whilst the second term depends on the value of the discount. Therefore, the final effect of a change in the current price is indeterminate. Expected change in profits Three different parts: the first one indicates the expected gain from an increase in the deadweight price from period t+1 to t+2, the second term provides the gain from the change in weight at period t+1 prices and the last term is the cost of the additional feeding.

11. 11 Empirical work: Data The data on the number of slaughtered animals were from the Scottish Government and covered the period 1997 to 2014 on a monthly basis. It was aggregated into four regions: North East, North West, South East and South West and all Scotland. This gave fifteen series to be analysed Scotland - Number of slaughtered steers1997 - 2014

12. 12 Empirical work: Data Scotland - Number of slaughtered heifers 1997 - 2014 Scotland - Number of slaughtered young bulls 1997 - 2014

13. 13 Empirical work: Data The price dataset was provided by the Agricultural and Horticultural Development Board (AHDB), which reports the deadweight cattle prices in pence per kilogram. As regards input prices, namely: cereals prices, compound feedingstuffs and veterinary costs, they were gathered from the Producer Prices of Agricultural Products published by DEFRA with base year 2005=100.

14. 14 Empirical work: Data ADF and PP tests were applied to the data and all series were found stationary, i.e., I(0).

15. 15 Empirical work: Methods Hendry’s General to Specific methodology (Hendry, 1995) was used to discover the dynamics of the relationships. An autoregressive distributed lag model (ADL) form for the estimation of beef supply responses was employed for the estimation Where a (L), b i (L) are lag polynomials and y t is the dependent variable and is the independent variable.

16. 16 Empirical work: Methods To facilitate its interpretation in terms of “long term” and “short term” responses, the model was reparametrized in terms of error correction models as follows: Where the operator Δ indicates that the variables are in difference. The coefficients α 2, α 3, α 5 and α 6 provide the short term dynamics γ0, γ1, and γ2 are the long term coefficients and λ is the adjustment coefficient.

17. 17 Empirical work: Results Fifteen supply-response models were constructed for heifers, steers and young bulls for all the regions North West (NW), North East (NE), South West (SW), South East (SE) and Scotland (All) as dependent variables. Data shows both trends and seasonality, therefore the equations were estimated considering appropriate trends and monthly seasonality dummies. Trend coefficients were highly significant for steers and heifers for the majority of areas. However, the sign shifts from positive to negative.

18. 18 Empirical work: Results Error correction terms results

19. 19 Empirical work: Results

20. 20 Empirical work: Results

21. 21 Empirical work: Results Importance of price expectations

22. 22 Conclusion Results showed that there is a significant variation in the supply response towards the current deadweight cattle price for all animal categories and spatial production allocation. Adjustment coefficients results showed high variability over animals and regions. The short term response to prices showed important variability. Differences on the sign of the response exist. The “long term” responses of the supply to changes in the current price were in most of the cases negative. Differences on the sign of the response exist.

23. 23 Conclusion Price expectations seem important for the farmers decisions of whether to market or not their cattle in the current period versus the next period. The results also open several areas for further research: To explore different price expectations schemes (e.g., adaptive or rational expectations) To expand the database to include then the number of animals purchased and their prices of live animals bought by finishers for fattening, which would allow estimating much richer models.

24. 24 Thank you for your attention!