agriculture in the uk seminar 27 june - welcome to … in the uk seminar 27 june 2016 defra policy...
TRANSCRIPT
Agriculture in the UK Seminar 27 June 2016
Defra Policy Overview
Vic Platten,
Food and Farming Strategy and Innovation
The Agri-Food Chain
3 - Icons made by Freepik from www.flaticon.com 3
Agriculture (The farm gate)
Manufacturing
Catering
Wholesale
Retail
Farming
70% of UK land (farming) in 2015
3.4m
0.4m
Food Farming
Farming 10 bn
Food 98 bn
Food and Farming 108Bn
7.2% of UK GVA in 2014
13.6% of total GB workforce in employment in 2015
Background – Farming structure
Sources: Agriculture in the UK 4
In 2013, 62% of holdings are operated by individuals
aged 55 or over
UK agriculture Labour (at June each year) 2015
Farmers, business partners, directors and spouses 293,730
Regular employees 115,459
Seasonal, casual or gang labour 67,263
Total workforce 476,452
In 2015, 46% of holdings were less under 20 hectares
in size.
These counted for only 4% of total hectares
0 < 60
60 < 70
70 < 80
80 < 90
90 < 100
100 < 110
110 < 120
120 < 130
130 < 140
140 < 150
150 < 160
160 < 170
170 < 180
180 and over
0
3
6
9
12
15
18
£ output per £100 input
Perc
enta
ge o
f fa
rms
Distribution of performance across
farms 14/15 UK
Many firms fail to recover their costs
for the year
Post Farm Gate
Icons made by Freepik from www.flaticon.com
Sources – Agriculture in the UK 5
Manufacturing Retail Catering Wholesale
Gross Value Added (2014)
£26.9 billion £30.2 billion £29.1 billion £11.9 billion
Employment (2015 GB)
0.40 Million 1.17 million 1.61 million 0.23 million
Trade
Source: Defra – Food statistics pocketbook 6
UK
EU
Rest of the world
Origins of food consumed in the UK (2014)
UK food production to supply ratio All food Indigenous type food
2014 62% 76%
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
£B
illio
n
UK trade in different food groups, 2014
Total Imports Total Exports
7
Defra Strategy
A world leading food and farming industry
A cleaner, healthier environment, benefiting people and the economy
A nation better protected against floods, animal and plant diseases and other hazards, with strong response and recovery capabilities
A thriving rural economy, contributing to national prosperity and wellbeing
Working internationally
Productivity
Data
Better Regulation
and…. • Delivery • How we’re organised • People and Professionalism
Taken from Defra Strategy, https://www.gov.uk/government/publications/defras-strategy-to-2020-creating-a-great-place-for-living , June 2016
8
A world leading food and farming sector
Growing the market:
becoming a global brand of
choice
Being more competitive:
adopting innovation,
best practice and
strengthening skills
Maintaining confidence
Developing resilience:
collaborating across value
chains
Taken from the main strands of the draft food and farming plan June 2016 – subject to revision
Context - Growing the market
Source: Anholt GfK Nation Brand Index, Visit Britain, Agriculture in the UK 9
Branding • National brand ranks third in the world, behind Germany and the USA. • Visit Britain study suggests UK is behind France and Italy for reputation of food and drink
Exports
Context
Since mid 1990s gap has widened.
Total value of imports currently over twice that of total exports.
Public Procurement • The incentive to procure to lowest possible costs, meeting minimum specifications, not
necessarily including other costs and benefits (e.g. environment or health)
0
5
10
15
20
25
30
35
40
45
Trade in food, drink and animal feed by SITC division (at 2015 prices) UK
Exports Imports
£ Billion
Context – Being more competitive
Source: Defra analysis of Eurostat and USDA data, Defra food chain productivity 10
0
50
100
150
200
250
Labour productivity in Agriculture
United States Netherlands
Italy France
United Kingdom
Index
(1993=100)
Key drivers of competitiveness: • Skills • R&D • Knowledge exchange • Investment • Reduced regulatory burden
-
20
40
60
80
100
120
140
160
Labour productivity in the post farm gate food chain
Manufacturing Wholesale Retail Catering
Index
(2000=1
00)
Context – Developing resilience Risk Management
• Production risks
• Market risks
• Finance risks
• Institutional risks
Source: OECD (2011) Price Volatility in Food and Agricultural Markets: Policy Responses, DECC GHG infographic 11
Change in commodity
prices
Resource efficiency
• Soil quality
• Water quality
• GHG emissions
• Biodiversity
Context- Maintaining confidence
• Effect is most noticeable when confidence falls • BSE, Horsemeat, salmonella
• An FSA survey from 2013, shortly after the horsemeat incident, suggested:
• 73% less confident in processed meat
• 71% less confident in general
• Impacts tend to be short, and acute to certain sectors
12
OUTLOOK FOR
AGRICULTURE
Graham Redman
The Andersons Centre
June 2016
FARM PROFIT AND CURRENCY
TIFF and £/€ Relationship
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0
5,000
10,000
15,000
20,000
25,000
30,000
35,0001
99
0
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
Exch
an
ge R
ate
- €
1 =
£
Tota
l In
co
me f
rom
Farm
ing
- £
m
TIFF per Entrepreneur
Exchange Rate
Source: DEFRA / ECB
-
2 000
4 000
6 000
8 000
10 000
12 000
100
110
120
130
140
150
160
170
1801
97
3
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
TIFF
£ B
illio
ns
TFP
Ind
ex, 1
97
3 =
10
0
Total factor productivity TIFF
PRODUCTIVITY AND PROFIT
TFP and TIFF Relationship
Source: DEFRA
TOTAL FACTOR PRODUCTIVITY
70
80
90
100
110
120
130
140
150
160
1701
97
3
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
TFP
Ind
ex, 1
97
3 =
10
0
All outputs All Inputs Total factor productivity
Source: DEFRA
MARKET VOLATILITIES
50
70
90
110
130
150
170
190
210
230
Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15
Ind
ex: 2
00
6 t
o 2
00
7 =
10
0
Milk Price
Wheat Price
Indexed Milk and Wheat prices
Source: AHDB
VOLATILITY ON THE BOTTOM LINE
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.02
00
5
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
Ind
ex t
akin
g 2
00
5 =
1
Loam Farm £/Ha Friesian Farm ppl
Business Surplus Volatility Index
Source: Andersons
COMPONENTS OF UK FARMING
OUTPUT 2015 2025 ~ Major factors
Combinable Crops 19% Loss of Active Ingredients
Roots 3% Decline of S. Beet area
Field Veg & Hort 10% Agri-Tech focus
Fruit 3% High Value Fruit Systems
Milk 16% Consolidation to fewer
Beef 15% Decrease of subsidy
Sheep 6%
Pigs 5% Antibiotics, Inc. pig meat demand
Poultry 13% Growth of global demand
Other 11% Strong rise out of commodities
And key factors for Medium Term
WOODLAND IN UK AGRICULTURE
0
200
400
600
800
1,000
1,200
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
20
17
20
19
20
21
20
23
20
25
'00
0 H
a
Woodland
IMPORTANCE OF GRASS IN UK
All Tillage
(ex. Grass, 29%
All Grass, 42%
Rough Grazing,
29%
Grass Vs Others
All Tillage (ex. Grass
All Grass
Rough Grazing
6200
6400
6600
6800
7000
7200
7400
19
93
19
96
19
99
20
02
20
05
20
08
20
11
20
14
20
17
20
20
20
23
'00
0 H
a
Grass Area Projection
GLOBAL MEAT PRODUCTION
By Species – 1961 to 2013
Source: FAO / Andersons
GLOBAL GDP
At Purchasing Power Parity – 1990 to 2016
Source: World Bank / Andersons
0
5,000
10,000
15,000
20,000
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
GD
P a
t P
urc
hasi
ng
Po
wer
Pari
ty -
$ b
illio
n
China Eurozone
UK USA
India
CHINESE TRADE
Commodity Trading (5 Year Average)
0
50,000
100,000
150,000
200,000
250,000
Wheat Soybean Maize Poultry Pigmeat Dairy Beef & Veal
'00
0 M
T
Imports Domestic Production
Source: USDA / Andersons
Import
Value:
$928m
Import Value:
$33,452m
$831m
$1,345m
$4,145m
$466m
Export Value:
$695m
FARMING HAS CHANGED
Growth of Farming Since the 1960's
Source: FAO / Andersons
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.001
96
0
19
62
19
64
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
20
12
20
14
Ind
ex o
f In
cre
ase
of
Pro
du
cti
on
Grains
Oilseeds
Fruit & Veg
Sugar
Farmed Land
Pig & Poultry Meat
Red Meat
OUTLOOK FOR
AGRICULTURE
Graham Redman
The Andersons Centre
June 2016
Markets and Outlook: UK Agriculture
Myles Patton, Siyi Feng and John Davis, AFBI
AGRICULTURE IN THE UNITED KINGDOM
SEMINAR, Defra, 27th June 2016
Overview of the presentation
• Background
• 2016 baseline projections – Deterministic
• 2016 baseline projections – Stochastic
• Summary
BACKGROUND
System of equations for the main
agricultural sectors in England,
Wales, Scotland and Northern
Ireland
FAPRI-UK models are
incorporated within FAPRI-
Missouri’s European Model
UK models solve simultaneously
with EU models
FAPRI Global
Model
UK Models
(E, W, S, NI)
FAPRI EU
Gold Model
FAPRI-UK Project
Baseline Assumptions
• Baseline assumes that policies that were in operation in
February 2016 remain in place for the duration of the
projection period (2016 to 2025)
• Incorporates:
– Macro projections from December from IHS Global Insight
– Latest CAP reforms
– Uruguay Round trade rules remain in place
– No anticipation of TTIP effects
• Normal weather
Fffffffffffffff
Macroeconomic Assumptions
2013 2014 2015 2016 2017 2025
Growth %
World 2.5 2.7 2.6 2.9 3.2 3.3
China 7.7 7.3 6.9 6.3 6.3 6.4
India 6.9 7.3 7.3 7.6 7.7 7.8
Brazil 3.0 0.1 -3.6 -2.4 1.2 2.3
U.S. 1.5 2.4 2.5 2.7 3.0 2.7
Exchange rate per U.S.$
China 6.1 6.2 6.3 6.6 6.7 6.7
India 58.6 61.0 64.1 67.1 67.1 66.0
Brazil 2.2 2.4 3.4 4.2 4.2 4.3
Population millions
World 7,176 7,261 7,346 7,432 7,517 7,600
China 1,361 1,368 1,374 1,381 1,387 1,392
India 1,279 1,295 1,311 1,327 1,343 1,358
Africa 1,125 1,154 1,183 1,213 1,244 1,275
World
Fffffffffffffff
Background: Macroeconomic Assumptions
2013 2014 2015 2016 2017 2025
Growth %
World 2.5 2.7 2.6 2.9 3.2 3.3
China 7.7 7.3 6.9 6.3 6.3 6.4
India 6.9 7.3 7.3 7.6 7.7 7.8
Brazil 3.0 0.1 -3.6 -2.4 1.2 2.3
U.S. 1.5 2.4 2.5 2.7 3.0 2.7
Exchange rate per U.S.$
China 6.1 6.2 6.3 6.6 6.7 6.7
India 58.6 61.0 64.1 67.1 67.1 66.0
Brazil 2.2 2.4 3.4 4.2 4.2 4.3
Population millions
World 7,176 7,261 7,346 7,432 7,517 7,600
China 1,361 1,368 1,374 1,381 1,387 1,392
India 1,279 1,295 1,311 1,327 1,343 1,358
Africa 1,125 1,154 1,183 1,213 1,244 1,275
World
Fffffffffffffff
Background: Macroeconomic Assumptions
World EU
Fffffffffffffff
Background: Macroeconomic Assumptions
World EU
Oil Price ($ Refiners' crude oil)
IHS Global
Insight to
2020
Held constant
in real terms
after 2020
2016BASELINE PROJECTIONS -
DETERMINISTIC
Fffffffffffffff
2016 Baseline Projections: Crop Sector
World prices lower than their historical peaks, but higher than pre 2007 levels
Exchange rate is important: weakening of euro against dollar in short-run exerts an
upward impact; but euro strengthens against the dollar in long-run
Historic Projected
World cereal prices
Historic Projected
EU cereal prices
-3000
2000
7000
12000
17000
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
1,0
00
ton
ne
s
UK Wheat Balance
Production Domestic Use Net Export
0
100
200
300
400
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
£/
ton
ne
UK Crop Prices
Wheat Barley Rapeseed
Fffffffffffffff
UK crop prices do not tail off to same extent as EU prices due to UK£ weakening
against the euro
The UK wheat sector:
Modest increase in both production and domestic use
Small surplus for export: trade position is sensitive to uncertainties, such as
yield growth
2016 Baseline Projections: Crop Sector
Projected
Fffffffffffffff
2016 Baseline Projections: Livestock Sector
Historic Projected
EU meat prices
Fffffffffffffff
UK beef price Increases in longer run due to increase in EU price and UK£ weakening
against euro
Decline in beef production due to falling beef and dairy cows
Widening gap between consumption and production
2016 Baseline Projections: UK Beef Sector
Historic Projected
0.0
50.0
100.0
150.0
200.0
250.0
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
£/1
00
kg
lwt
UK Beef Price
-500
0
500
1000
1500
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
1,0
00
ton
ne
s
UK Beef Balance
Production Domestic Use Net Export
Fffffffffffffff
Modest recovery in the beginning of projection period, then plateau at a level close
to the average of recent years
2016 Baseline Projections: Dairy Sector
Projected
World dairy commodity prices
Projected
EU dairy commodity prices
Fffffffffffffff
Modest recovery in farm gate milk prices, but well below 30 ppl
Projected UK milk production fairly flat
2016 Baseline Projections: Dairy Sector
Historic Projected
0
10
20
30
40
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
pe
nc
e p
er
litr
e
UK Regional Farm Gate Milk Prices
England Wales Scotland Northern Ireland
0
5000
10000
15000
20000
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
ml
UK Milk Production
England Wales Scotland Northern Ireland
Historic Projected
2016 BASELINE PROJECTIONS -
STOCHASTIC
Fffffffffffffff
Developing Stochastics Within the FAPRI-UK Model
Fffffffffffffff
Developing Stochastics Within the FAPRI-UK Model
• Uncertainty sources considered: Crop yields;
Meat demand; &
Macro-economic conditions (oil prices, GDP and exchange rates) and
world agricultural commodity prices
Fffffffffffffff
Developing Stochastics Within the FAPRI-UK Model
• Uncertainty sources considered: Crop yields;
Meat demand; &
Macro-economic conditions (oil prices, GDP and exchange rates) and
world agricultural commodity prices
Distribution and correlations within groups
Fffffffffffffff
Developing Stochastics Within the FAPRI-UK Model
• Uncertainty sources considered: Crop yields;
Meat demand; &
Macro-economic conditions (oil prices, GDP and exchange rates) and
world agricultural commodity prices
Distribution and correlations within groups
• Focus on key elements that impact both supply and
demand side uncertainty Resulting price and quantity distributions are acceptably consistent
with historical observations
Fffffffffffffff
Developing Stochastics Within the FAPRI-UK Model
• Uncertainty sources considered: Crop yields;
Meat demand; &
Macro-economic conditions (oil prices, GDP and exchange rates) and
world agricultural commodity prices
Distribution and correlations within groups
• Focus on key elements that impact both supply and
demand side uncertainty Resulting price and quantity distributions are acceptably consistent
with historical observations
• 500 sets of random draws are made and in turn fed to the
modelling system to solve
FAPRI-UK stochastic projections
UK Wheat Price – allowing for uncertainty
Model simulated 500
times
(10 shown here)
UK Wheat Price – allowing for uncertainty
FAPRI-UK stochastic projections
UK Wheat Price – allowing for uncertainty
Percentiles
FAPRI-UK stochastic projections
UK Beef Price – allowing for uncertainty
FAPRI-UK stochastic projections
UK farm gate milk price – allowing for uncertainty
FAPRI-UK stochastic projections
FAPRI-UK stochastic projections
Summary
Most prices are projected to be at the average of their historic levels.
In the wheat sector, the UK remains net exporter but the amount of net
export is small.
In the beef sector, net import is projected to increase.
Modest recovery in price is projected in the dairy sector. The 10%
percentile in the stochastic projection is close to current milk prices. UK
milk production is projected to be flat.
Summary (cont.)
The deterministic baseline of agricultural sector projections is fairly flat
but useful.
Starting point of our modelling practice
Isolates the main drivers for the long run
Annual baseline meetings with industry are held to make sure we
take on board important developments within the sector
The stochastic baseline illustrates the impacts of the uncertainties in the
environment (weather, macroeconomic conditions, etc.) that the
agricultural sector operates in.
Policy analysis involves both the deterministic and stochastic models.
Farm tenure in England: does it have an impact on farm efficiency?
Presentation to AUK seminar 2016
Mario Deconti
Head of Food & Farming Economics
Contents. This presentation will cover:
1. Background: what do we know about the pattern of tenure in England?
2. Does tenure have an impact on farm efficiency? A look at cereal, dairy and grazing livestock farms.
3. Conclusions and discussion.
59
Across England as a whole, 60% of farms are owner occupied – though this can vary by region.
60 Source for chart: Defra June Survey of Agriculture, 2015. Data excludes seasonal lets of less than one year.
However, although they are by far the most common farm type, owner occupied farms tend to be much smaller on average than tenanted and mixed tenancy farms.
61
In most sectors, the majority of farms are fully owner occupied, with the exception of dairy and cereal farms
62 Source for chart: Defra June Survey of Agriculture, 2015. Data excludes seasonal lets of less than one year.
Despite accounting for 60% of holdings, owner occupied farms only account for 40% of all land farmed overall, though this can vary significantly by sector.
63 Source for chart: Defra June Survey of Agriculture, 2015. Data excludes seasonal lets of less than one year.
Overall, the majority of farmers are aged 55 and over. Owner occupied farmers are the oldest group, with an average age of around 5 years higher than tenanted farmers.
64 Source for chart: Farm Business Survey, 2010/11 – 2014/15 five year matched sample.
In most sectors, output per hectare does not differ significantly by tenancy type
65
Of the land based farm types the dairy sector produces the highest average output per hectare. Generally, fully tenanted farms produce the least amount although this does not hold true for general cropping farms. This may be due to land being rented specifically for the production of
high value output crops such as potatoes and field vegetables.
Source for chart: Farm Business Survey, 2010/11 – 2014/15 five year matched sample.
The cost of rent can be a significant proportion of a tenanted farm’s overall input costs
66 Source for chart: Farm Business Survey, 2010/11 – 2014/15 five year matched sample.
Contents. This presentation will cover:
1. Background: what do we know about the pattern of tenure in England?
2. Does tenure have an impact on farm efficiency? A look at cereal, dairy and grazing livestock farms.
3. Conclusions and discussion.
67
What do we mean by efficiency?
Efficiency is concerned with the optimal production and distribution of scarce resources. There are different types of efficiency, but today we are interested in technical efficiency.
68
Technical efficiency occurs when firms use inputs in their most optimal way to produce as much output as possible. Technically efficient firms cannot combine their inputs in a different way to produce more output.
We will look at the results of 3 studies conducted by the Defra Observatory, between 2011 and 2013, which analyse the efficiency of cereal farms (using data for 2004-2008), dairy farms (using data for 2003-2010) and grazing livestock farms (using data for 2003-2009).
All studies are publicly available here:
https://www.gov.uk/government/collections/agricultural-productivity-and-competitiveness-analyses
Cereal farms
69
Owner occupied farms produce a higher level of output compared to tenanted farms, for the same value of inputs. This is not surprising, since rental costs are included in the value of inputs for tenanted farms.
70
Where farms are mixed tenancy, mainly tenanted farms perform nearly as strongly as the mainly owner occupied cereal farms. In terms of agricultural output, the difference is not statistically significant.
Dairy farms
71
Again, owner occupied farms produce a higher level of output, compared to tenanted farms, for the same value of inputs. Farms with Full Agricultural Tenancies (FAT) perform better than those with Farm Business Tenancies (FBT).
72
Is it just rental payments that is driving the differences between owner-occupiers and tenants? Removing agricultural property costs from the model reduces the gap between owner occupiers and tenant farmers, but the difference is still statistically significant. This suggests that something else might be driving the results. One possibility is dairying can require significant capital investment, which is generally easier for owner occupiers who can borrow against the value of their land to finance it; and because tenant farmers may have to obtain their landlord’s consent for any investment.
The difference between FAT and FBT is only just statistically significant
Grazing livestock farms
73
The impact of tenure type on economic performance is more complicated for grazing livestock farms, since this also depends on both farmer age and farm size.
74
Farms of both tenancy types perform similarly well until the farmer reaches age 65, when output clearly begins to fall on owner-occupier farms. On tenanted farms, a 65 year old farmer produces as much as a 45
year old famer; with only a modest drop in output at age 75.
Smaller farms tend to perform equivalently, with small, but statistically significant differences in efficiency only becoming apparent on larger farms, where tenanted farms produce slightly more than owner occupied farms.
75
Contents. This presentation will cover:
1. Background: what do we know about the pattern of tenure in England?
2. Does tenure have an impact on farm efficiency? A look at cereal, dairy and grazing livestock farms.
3. Conclusions and discussion.
76
The relationship between tenure and performance is not straightforward…
• In some sectors, owner occupier farms produce more, but are they really more ‘efficient’?
• 9%-11% of tenant farms’ costs are rent – a fixed cost that does not vary with production levels. However, they are typically not 9%-11% behind owner occupied farms in terms of the value of their output.
• This may be an overly simplistic view. In reality, farm efficiency is influenced by a range of factors that interact with the type of farm business model:
• Farmer age: owner occupiers may be able to take semi-retirement, whereas tenants need to remain productive to pay the rent.
• Sector: in some sectors (e.g. dairy), small margins mean that farms can only survive if they are fully efficient.
• Capital intensity: in general, it may be easier for owner-occupiers to fund large capital investments.
• Land quality: does this differ between farm types and is it reflected in rent prices?
• The individual famer’s outlook: is farming a lifestyle choice or a profit maximising business?
77
Other farm business models
• Owner-occupied and tenancies remain the predominant business models in farming
• Other models that offer new entrants a route into farming include:
• Contracting
• Share farming and joint ventures
• Franchise farming
• Agri-land partnerships / co-operatives
• But more evidence is needed on their uptake and efficiency
• Interested in exploring these business models further, are they needed to help facilitate new entrants? and what would encourage their uptake?
78
Ambitious for farm level co-operation: Where are we now and where could we be?
Jack Watts
Lead Analyst, AHDB
Where are we now?
Farm level co-operation Su
pp
ly c
hain
co
-op
era
tio
n
Combinable
crops
Potatoes
Beef & Lamb
Poultry
Pigs
Dairy
Drivers and barriers
Drivers
Competitiveness
Resilience
Land tenure
Barriers
Independence
Lifestyle farming
Vertical - WIIFM
Example: Brixworth Farming Co
Key metrics Pre-BFC BFC
Acres 4.2K 4.2K
Combines 4+ 2
Tractors at harvest 15 6
Total horsepower at
harvest
2035 1000
Telehandlers 5 1
Total units of labour 17+ 8
What makes Brixworth work?
Business and corporate structure
Performance
Monitoring
Risk Management
Distinguishes between strategy and operations
Has the right team – knows when to bring in external expertise. Then joins it all up.
What could the future look like?
More end-to-end risk management in supply chains as seen in the poultry industry now?
Collective ownership schemes to minimise capital outlay e.g. could multiple businesses own one herd of cows to maximise technical gains, efficiency and flexibility?
Could we see cross sector solutions to specific challenges e.g. The beef sector needs more flexibility in its systems & the arable sector needs more break crops?
Conclusions
UK agriculture needs to explore new business models to build competiveness in a global market place and improve resilience against uncertainties. Innovative co-operation could be a part of this.
The challenges facing co-operation are complex – both economic and social.
Clarity, transparency and good governance are likely to be underpinning success factors of co-operation.