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

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

22

Outline

1.Motivation

2.Purpose

3.State of the art

4.Theoretical model

5.Data

6.Method

7.Results and discussion

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 % Steers Heifers Young bulls

NW 1.37 2.95 0.62

NE 36.23 36.56 18.45

SW 48.55 47.28 43.85

SE 13.85 13.21 37.08

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.

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 run Long runReutlinger (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) + +

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.

77

Structure of the theoretical model

Period t Period t+1 Period t+2

Cattle are purchased Based on the current Farmer will sell the cattlebased on expected profits price paid on the market, not sold in period t+1

the cost of feeding andexpected prices of

cattle in period t+2, thefarmer decides to

sell in period t+1 or in t+2

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

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

1*2

1

tt

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.

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AAOtherwise

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1

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1

,

0,01

ttttttttt sFrFrFWP

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*2

11

ttttttt sFrFWP 11

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

tP

ts

tt FW

tF tr

1010

Formulation of the theoretical model

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* 11

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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.

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1

* 11

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.

1111

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

1212

Empirical work: Data

Scotland - Number of slaughtered heifers 1997 - 2014

Scotland - Number of slaughtered young bulls 1997 - 2014

1313

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.

1414

Empirical work: Data

Units Mean St. Dev. Min Max Skewness Kurtosis

North West Steers heads 599.8 220.5 155 1136 -0.05 2.25 Heifers heads 918.2 316.7 309 1617.0 0.02 2.20 Young bulls heads 10.2 15.0 0 104.0 2.24 10.6North East Steers heads 7965.4 1486.2 4062 12028 0.10 3.07 Heifers heads 6068.5 1443.3 3214 9916.0 0.46 2.59 Young bulls heads 1069.7 676.5 3 3385 0.94 3.68South West Steers heads 8331.0 1283.2 5244 12111 0.48 2.99 Heifers heads 6777.8 1247.1 4454 12184 0.65 4.22 Young bulls heads 1132.0 686.1 5 3734 1.31 4.64South East Steers heads 3099.2 1074.9 1433 6743 1.12 3.72 Heifers heads 2208.5 1296.2 852 6986 1.77 5.42 Young bulls heads 1339.2 564.4 0 3335 0.52 3.60Scotland Steers heads 19995.3 2813.8 13678 28153 0.43 3.16 Heifers heads 15973.0 3107.0 9954 25415 0.51 3.02 Young bulls heads 3551.0 1649.2 16 9123 0.93 3.67Prices Steers p/kg 118.5 18.3 96.2 167.2 0.97 2.84 Heifers p/kg 117.2 18.4 96.4 165.6 0.97 2.76 Young bulls p/kg 110.4 17.6 86.6 161.4 0.92 2.91 Compound feedingstuffs Index 87.1 23.2 65.1 140.0 0.88 2.34 Veterinary services Index 88.8 9.4 78.6 113.2 0.97 2.55 Feed barley Index 96.4 37.4 52.9 198.8 1.04 2.75

Source: based on Agricultural and Horticultural Development Board (AHDB), Regional Slaughterings Summary (Scottish Government) and Index of Producer Prices of Agricultural (DEFRA).

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

1515

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) , bi (L) are lag polynomials and yt is the dependent variable and is the independent variable.

t

N

iitit xLbyLa

11

1616

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 α2, α3, α5 and α6 provide the short term dynamics γ0, γ1, and γ2 are the long term coefficients and λ is the adjustment coefficient.

ttttttt zxxxyyy 62514322110

121

61

21

543

21

0121

615312

1111 ttt

ttttt

zxy

zxxyy

121101615312 tttttttt zxyzxxyy

1717

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.

1818

Empirical work: Results

Error correction terms results

1919

Empirical work: Results

Adjustment coefficient (λ)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Ste

ers

- N

W

Ste

ers

- N

E

Ste

ers

- S

W

Ste

ers

- S

E

Ste

ers

- A

ll

Hei

fers

- N

W

Hei

fers

- N

E

Hei

fers

- S

W

Hei

fers

- S

E

Hei

fers

- A

ll

Y. b

ulls

- N

W

Y. b

ulls

- N

E

Y. b

ulls

- S

W

Y. b

ulls

- S

E

Y. b

ulls

- A

ll

2020

Empirical work: Results

Short term reponse of animals with respect to current price (α3)

-2

-1

0

1

2

3

4

5

Ste

ers

- N

W

Ste

ers

- N

E

Ste

ers

- S

W

Ste

ers

- S

E

Ste

ers

- A

ll

Hei

fers

- N

W

Hei

fers

- N

E

Hei

fers

- S

W

Hei

fers

- S

E

Hei

fers

- A

ll

Y. b

ulls

- N

W

Y. b

ulls

- N

E

Y. b

ulls

- S

W

Y. b

ulls

- S

E

Y. b

ulls

- A

ll

2121

Empirical work: Results

Long term reponse of animals with respect to current price (γ3)

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

Ste

ers

- N

W

Ste

ers

- N

E

Ste

ers

- S

W

Ste

ers

- S

E

Ste

ers

- A

ll

Hei

fers

- N

W

Hei

fers

- N

E

Hei

fers

- S

W

Hei

fers

- S

E

Hei

fers

- A

ll

Y. b

ulls

- N

W

Y. b

ulls

- N

E

Y. b

ulls

- S

W

Y. b

ulls

- S

E

Y. b

ulls

- A

ll

Importance of price expectations

2222

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.

2323

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.

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Thank you for your attention!