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Analysing reduced tillage practices within a bio-economic
modelling framework
Toby J. Townsend, Stephen J. Ramsden, Paul Wilson ⁎
Division of Agricultural and Environmental Sciences, University of Nottingham, Sutton BoningtonCampus, College Road, Sutton Bonington, Loughborough LE12 5RD, United Kingdom
abstractarticle info
Article history:
Received 22 September 2015
Received in revised form 15 April 2016
Accepted 18 April 2016
Available online 29 April 2016
Sustainable intensificationof agricultural productionsystems will requirechanges in farm practice. Withinarable
cropping systems, reducing the intensity of tillage practices (e.g. reduced tillage) potentially offers one such sus-
tainable intensification approach. Previous researchers have tended to examine the impact of reduced tillage on
specific factors such as yield or weed burden, whilst, by definition, sustainable intensification necessitates a
system-based analysis approach. Drawing upon a bio-economic optimisation model, ‘MEETA’, we quantify
trade-off implications between potential yield reductions, reduced cultivation costs and increased crop protec-
tion costs. We extend the MEETA model to quantify farm-level net margin, in addition to quantifying farm-
level grossmargin, net energy, and greenhouse gas emissions. For thelowest intensity tillage system, zero tillage,
results demonstrate financial benefits over a conventional tillage system even when the zero tillage system in-
cludes yield penalties of 0–14.2% (across all crops). Average yield reductions from zero tillage literature range
from 0 to 8.5%, demonstrating that reduced tillage offers a realistic and attainable sustainable intensification
intervention, given the financial and environmental benefits, albeit that yield reductions will require more
land to compensate for loss of calories produced, negating environmental benefits observed at farm-level.
However, increasing uptake of reduced tillage from current levels will probably require policy intervention; an
extension of the recent changes to the CAP (‘Greening’) provides an opportunity to do this.
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Keywords:
Reduced tillage
Bio-economic modelling
Sustainable intensification
1. Introduction
In the face of a growing world population, increased resource scarci-
ty and the challenges of climate change mitigation, there is an increas-
ing need for adaptation in agriculture and agricultural systems
towards practices that lead to “Sustainable Intensification”(SI; Wilson,
2014). Within arable systems dominated by combinable crop produc-
tion (e.g. wheat, oilseed rape), changes to cultivation practices, for
example towards reduced tillage
1
(RT), conservation tillage or zero tillage
(ZT), have the potential to provide multiple environmental benefits
(Holland, 2004) that would contribute towards SI objectives. These
cultivation practices do not involve soil inversion (which occurs with
ploughing); however the extent of soil disturbance typically ranges
from intensive deep RT (e.g. tine harrows) to very minor soil distur-
bance in ZT (e.g. direct drilling).
RT provides benefitsin areas prone to soil erosion including reduced
soil erosion, pesticide runoff and watercourse sedimentation, improved
soil quality, reduced leaching of nutrients and lower greenhouse gas
(GHG) emissions (Fawcett and Towery, 2002; Holland, 2004; Morris
et al., 2010). In humid temperate regions, such as northwest Europe,
soil erosion is less of a problem and the environmental benefits of
RT systems are less certain (Davies and Finney, 2002). RT systems
have, however, been found to have lower GHG emissions and more
favourable energy balances because of a reduction in machinery use
(e.g. Knight, 2004). Reduced machinery use also leads to cost savings
(Vozka, 2007), which is the primary driver of RT use in these areas
(Davies and Finney, 2002). Studies have specifically identified that RT
has lower fuel costs (e.g. Sijtsma et al., 1998; Šarauskis et al., 2014).
Fewer machinery operations are also required with RT leading to
reduced labour costs and improved timeliness of crop operations
(Morris et al., 2010). When comparing RT with conventional tillage
(CT) Verch et al. (2009) identified increased net returns from a German
RT system of approximately €100 ha
−1
.
Whilst clear financial benefits of RT practices have been observed,
crop yieldeffects are less clear. Van den Putte et al. (2010),inreviewing
Agricultural Systems 146 (2016) 91–102
Abbreviations: CAP, Common agricultural policy; CT, conventional tillage; DRT, deep
reduced tillage; GHG, greenhou se gas; GM, gross margin; NE, net energy; NM, net
margin; WOSR, oilseed rape; RT, reduced tillage; RP, rotational ploughing; SI, sustainable
intensificatio n; SRT1, shallow reduced tillage 1; SRT2, shallow reduced tillage 2; SB,
spring barley; WB, winter barley; WFB, winter field beans; WW, winter wheat; ZT, zero
tillage.
⁎Corresponding author.
E-mail address: paul.wilson@nottingham.ac.uk (P. Wilson).
1
Practitioners use a varietyof names for the non-inversion tillage system. In this study,
reduced tillage is used to referto any tillage system that does not employ inversion. For a
more detailed look at variousterminologiesused in the literature, seeTable 1 in Townsend
et al. (2016).
http://dx.doi.org/10.1016/j.agsy.2016.04.005
0308-521X/© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Agricultural Systems
journal homepage: www.elsevier.com/locate/agsy
Europe-wide field experiments, found an average yield reduction of
4.5% from RT (from 563 observations across different experimental
years) though when ZT was considered individually there was an aver-
age yield penalty of 8.5% (171 observations). Arvidsson et al. (2014)
found an average yield reduction of 1.8% from shallow RT experiments
in Sweden (918observations) and 9.8% lower for ZT (226 observations).
Crop-specific effects of RT are confirmed by Van den Putte et al. (2010)
with winter cereals and maize responding unfavourably to RT whilst the
yields of other crops were unaffected. Climate-specific effects have been
found, with a meta- analysis by Ogle et al. (2012) reporting reductions in
yield for ZT systems for wheat and maize in the Northeast of the US, but
increased yields in more southerly areas. Although RT tends to show an
average yield reduction, when individual field experiments are consid-
ered, yields can be greater than with inversion-based tillage (e.g.
Knight, 2004; Verch et al., 2009).
Although fuel, labour and machinery costs have been estimated to
be lower for RT systems, there can be additional costs in RT systems
resulting from greater weed, pest and disease burdens. Where present
or where there is perceived to be a risk of their presence, farmers will
apply additional crop protection inputs. Generally, extra herbicide is
required for weed control under RT (Melander et al., 2013). Models of
RT system costs have accounted for input use variability and have con-
cluded that reduced fuel costs outweigh the costs of additional pesticide
inputs (e.g. Lafond et al., 1993; Nail et al., 2007; Vozka, 2007). Greater
amounts of fungicides may also be required, depending on the preced-
ing crops in the rotation (Bürger et al., 2012). The fate of crop residues
also influences tillage system costs as leaving crop residues in situ in
RT systems can potentially increase molluscicide and fungicide require-
ments (Soane et al., 2012).
Consequently, whilst RT within a northwest European context pro-
vides possible cost and GHG savings, the potential trade-offs of RT ap-
proaches include yield reductions and increased crop protection costs.
Currently, approximately 30–40% of arable land in England is under RT
(Defra, 2010; Townsend et al., 2016). Given the identified benefits asso-
ciated with the technique, it is pertinent to determine why there isnot a
greater area of land under RT.
Previous studies noted abovehave largely focused upon single issues
of relevance to RT (e.g. profit; Verch et al., 2009); however, to achieve SI
objectives it is necessary toexamine the changes to cropping system ap-
proaches within a wider, system-based context. Sørensen et al. (2014)
used a system-based approach to investigate tillage practices, demon-
strating the value of this approach. This current study aims to address
this issue, specifically utilising a bio-economic model, building upon
Glithero et al. (2012), to investigate the influence of tillage type on a
farm system and its outputs. Within our approach, we quantify the
benefits, trade-offs and costs associated with different cultivation and
crop establishment practices within a UK arable farm context.
2. Methodology
2.1. MEETA model
The MEETA (Managing Energy and Emissions Trade-Offs in Agricul-
ture) model is a bio-economic optimisation model that determines op-
timal crop mix for three primary objectives: profit and net energy (NE)
maximisation, and GHG emission minimisation. Profit is measured by
total gross margin (GM), i.e. value of sales less variable costs of produc-
tion for a given harvest year. Output from runs under each objective al-
lows comparison of trade-offs between these competing objectives: for
example, how much profit is foregone from reducing GHG emissions.
The model was originally developed to establish trade-offs associated
with increasing the supply of agricultural feedstocks for bioenergy pro-
duction (Glithero et al., 2012). Themodel has also been used to consider
the economic and environmental impacts of including dedicated energy
crops (miscanthus and short rotation coppice grown for biofuel
feedstock) within farm cropping systems and the extent to which mar-
ginal land is suited to bioenergy feedstock production (Glithero et al.,
2015).
The model used here excludes dedicated energy crops and considers
a 400 ha farm with a crop rotation that can include any of the following:
winter wheat (WW), winter and spring barley (WB and SB, respective-
ly), winter oilseed rape (WOSR) and winter field beans (WFB). The WW
crop includes first, second and continuous wheats, i.e. first wheat is a
wheat crop grown after a break crop (in the model this would be
WOSR or WFB); second wheat is a wheat crop after first wheat and con-
tinuous wheat is where land is under wheat for three or more years.
Straw can be baled from WB, SB and WW, or incorporated into the
soil. Rotational constraints within the model limit the crops that can
be grown, with break crops (WOSR and WFB) only being grown after
a cereal crop. The crop mix generated is a single year representation of
the average area of each crop grown.
A brief description of the three primary metrics of interest (GM, NE,
GHG emissions) is given below; further details are provided in Glithero
et al. (2012). The GMs include the variable costs of fertiliser, crop pro-
tection, seed, fuel for machinery operations and grain drying, and con-
tractors' fees. Note that these GMs do not include the Basic Payment
Scheme subsidy, part of the Common Agricultural Policy (CAP), as this
is decoupled from production and therefore will not vary with crop
mix. However, recent changes to the CAP (‘Greening’) do effect produc-
tion and are included in the methods described below.
NE takes account of the energy required to produce the inputs, as
well as the energy embedded in the machinery being used and the en-
ergy captured within the crop output. GHG emissions are calculated
from the emissions required to produce fertilisers and sprays, the em-
bedded emissions from machinery, soil N
2
O (nitrous oxide) emissions
(calculated as 1.6% of applied nitrogen (N) released as N
2
Oandaback-
ground soil emission of 1.4 kg N
2
O–Nha
−1
yr
−1
). In reviewing the ZT
literature, Soaneet al. (2012) found that ZT tends to initially have higher
N
2
O emissions but that this is not a consistent finding. Therefore, the
emission level was initially kept constant for all tillage systems
modelled. A sensitivity analysis was used to assess how important
these assumptions are to overall GHG emissions for the different ZT sys-
tems considered below.
It was assumed that tillage practices do not influence fertiliser or
crop protection requirements. Reducing tillage intensity has been sug-
gested to alter fertiliser requirements. Some sources have found that
greater N application is required during the first years of ZT and lower
amounts in later years —in part because of reduced leaching (Soane
et al., 2012); however, there is insufficient data to robustly consider
this and, moreover, effects are likely to be highly site- and farm
system-specific; they are, therefore, not included in the model.
The original model contains an intensive conventional tillage (CT)
process consisting of a single pass of a plough followed by two passes
of a power harrow. Work-rates for different machinery operations
(ABC, 2011) are based on a heavy soil type and thus represent a relative-
ly energy-intensive tillage system. The CT system used in the original
model was modified to reflect a range of different RT systems. A number
of scenarios were considered to provide a systems approach to deter-
mining the value of RT systems. These are listed below but more details
are given in the further sections of the methodology.
•Baselinescenario: In this scenario, the model parameters and assump-
tions reflected market conditions in 2011, which is identical to those
in the original study (Glithero et al., 2012). These prices were specifi-
cally maintained to allow a direct comparison to the outputs present-
ed in the previous work, withthe current work, without the conflating
effect of introducing more recent prices. All model scenarios,
excludingthe price sensitivity scenario, are based on the 2011 market
conditions.
•Net margin scenario: To capture tillage system impacts on farm fi-
nances, total farm net margin (NM) was calculated as GM less
92 T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
machinery costs (ownership and running costs) and labour costs. This
was calculated for each RT system based on the optimised crop mix
and associated machinery usage for profitmaximisation.
•Yield penalty scenario: To consider yield impacts from RT systems, the
trade-off points were determined where yield penalties negate any
benefit for the ZT system over the CT system. This took into account
any potential extra land required to maintain overall production
levels and the GHG emissions associated with extra land. This was
conducted for the ZT system as it is the most different to CT and the
system most likely to suffer yield penalties.
•Weed control scenario: As RT systems, in particular ZT, are associated
with an increased weed burden. The additional costs for herbicides
were considered for the ZT system. The place of spring barley within
the rotation was considered with respect to weed control through
stale seedbeds.
•‘Greening’scenario: Rotational constraints were added to consider the
impact of the ‘Greening’requirement of the Basic Payment Scheme.
This was conducted for the CT and ZT systems.
•Price sensitivity scenario: To assess how prices influence the RT
systems, price sensitivity considered the crop mixes and outputs for
market conditions in 2014. This included the ‘Greening’rotational
requirements.
2.2. Baseline scenario
RT systemsin England employ a wide range of equipmentand tillage
practices; in particular, there is variation in tillage depth and number of
passes (SMI, 2005). These vary with soil and weather conditions, crops
and crop positions in rotations. To consider this level of variability, a
number of different RT systems are compared within our approach
(Table 1). These range in intensity from highly intensive in deep re-
duced tillage (DRT), to low intensity for two shallow reduced tillage
practices (SRT1 and SRT2), to negligible soil impact for ZT. There is
also a rotational ploughing (RP) system where both ploughing and
SRT are used within the rotation.
Work-rates for DRT and SRT2 were taken directly from ABC (2011),
whereas the work-rate for SRT1 was calculated from average contrac-
tors' work-rates. The number of cultivation passes is two for all crops
and tillage options apart from DRT, wherein WFB only has a single
pass (Table 2). The one-pass cultivator requires a large tractor, whereas
the disc andtine harrows require a medium tractor. There is no second-
ary tillage and it is assumed that two passes provide a tilth suitable for
establishment of the next crop. All tillage options include the same
drill as the original model, apart from WOSR, which is assumed to be
broadcast simultaneously with the tillage operations in all RT options
except for ZT where the seed drill is required.
2
The indirect energy
and GHG emissions are calculated based on the weight of the equip-
ment assuming that it is constructed of steel (Table 3).
2.3. Net margin scenario
The NM is similar to theadjusted GM used in Glitheroet al. (2015) to
investigate ownership of machinery; however, within the Glithero et al.
(2015) study, depreciation was calculated irrespective of machinery
usage rate. The NM used in this current analysis includes depreciation
and labour costs, which were adjusted to reflect machinery usage,
the inclusion of which provides a more realistic NM metric. This is
important for the current study as overall machinery use will de-
pend on the type of tillage operations used as well as the crop mix.
Non-tillage and drilling costs, such as fertiliser application and pes-
ticide spraying, were assumed to be unaffected by the type of tillage
system employed.
Machinery purchase prices and labour costs were taken from
ABC (2011), whereas depreciation rates, spares & repairs costs,
and insurance rates were taken from ABC (2001). The depreciation
rate was taken as a straight-line depreciation with the rate calculat-
ed based on the hours of machinery used; for example, the annual
depreciation rate for a medium tractor ranges from 15% for a use
of 500 h yr
−1
to 27% for a use of 1500 h yr
−1
.Interestoncapital
was assumed to be 3%.
2.4. Yield penalty scenario
To investigate whether potential yield penalties from RT outweigh
any potential cost saving benefits, sensitivity analysis was undertaken
to determine acceptable threshold yield penalties. Whilst actual yield
penalties for RT in England can be found in the literature, these fre-
quently relate to data obtained prior to the ban of stubble burning in
England and Wales (The Crop Residues (Burning)Regulations 1993); in
these studies the straw was burnt prior to the next crop, which is likely
to have aided weed and disease control. Yields from shallow RT are
lower after straw incorporation rather than straw burning (Graham
et al., 1986; Christian et al., 1999), suggesting RT systems are now less
favourable following the straw burning ban. Other evidence suggests
that WW yields tended to be lower under RT (Turley et al., 2003),
whereas Knight (2004) found higher yields under RT. Hence, given
Table 1
Tillage systems investigated in the MEETA model in decreasing levels of intensity.
Tillage system Abbreviation Description
Rotational ploughing RP Reduced tillage (two passes of a medium
disc harrow) for break crops but CT before
wheat and barley
Deep reduced tillage DRT A one-pass cultivator, consisting of tines
and discs. As the soil is heavy, we assume
two passes.
Shallow reduced
tillage 1
SRT1 Two passes of a medium disc harrow
Shallow reduced
tillage 2
SRT2 Two passes of a spring-tine harrow
Zero tillage ZT Seed planted into the stubble from the
previous crop
Table 2
Work-rates presented as the time inminutes required for a single pass over a hectare and
number of passes required per crop by tillage system.
Tillage
system
Field operation Work-rate
(min ha
−1
)
Number of passes per crop
WW WOSR WB SB WFB
CT Plough (6 furrow;
heavy land)
70 1 1 1 1 1
Power harrow 4 m;
heavy land)
67 2 2 2 1 0
Precision drill 43 1 1 1 1 1
RP Plough (6 furrow;
heavy land)
70 1 0 1 1 0
Power harrow 4 m;
heavy land)
67 2 0 2 1 0
Medium disc (2–3m) 42 0 2 0 0 2
Precision drill 43 1 0 1 1 1
DRT One-pass cultivator
(4.5 m; heavy land)
24 2 2 2 2 1
Precision drill 43 1 0 1 1 1
SRT1 Medium disc (2–3m) 42 2 2 2 2 2
Precision drill 43 1 0 1 1 1
SRT2 Spring-tine harrow
(6 m; heavy land)
23 2 2 2 2 2
Precision drill 43 1 0 1 1 1
ZT Precision drill 43 1 1 1 1 1
2
The originalmodel assumes that a precision drill is used; this has a slower work-rate
than the cultivation drill often used for drilling crops in cereal rotations. However,
substituting in a cultivation drill has verylittle impact on overalloutput values and, there-
fore, the precision drill was retained for this analysis.
93T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
this variability in data, the impact of yield reductions was examined by
reducing yields for all crops from the 100% baseline to establish the
threshold at which the benefits derived from reduced machinery use
over the CT system are negated.
2.5. Weed control scenario
As RT systems tend to be associated with additional herbicide costs,
this is considered in the model. In general, a multi-purpose herbicide
can be applied prior to planting for control of weeds. However, where
weeds have become established and have started developing herbicide
resistance, more specialist herbicides are required. The most problemat-
ic weed in the UK is black-grass and there are a number of different her-
bicides commercially used to control it. Lutman et al. (2013) suggests
that farmers are spending between £30 and £85 ha
−1
for herbicides
to control black-grass. In the model a scenario was considered wherein
black-grass herbicides are applied every other year at a cost of £85 ha
−1
based on theextreme value given by Lutman et al. to reflect the typically
greater use of herbicides in RT systems.
One method for controlling weeds in cropping systems is through
stale seedbeds and later-sown crops. The MEETA model includes the
option for the farm to grow spring barley (SB); however, this was not se-
lected by the original model because of its low yield. Sensitivity analysis
was used to examine the level of yield penalty required (in other crops)
before SB, without a yield penalty, was selected by the model.
2.6. ‘Greening’scenario
Recent changes introduced as part of the Basic Payment Scheme of
the EU's Common Agricultural Policy (CAP) mean that some of the orig-
inal optimal crop mixes within the optimised MEETA model would not
now be allowed. To gain the full payment, Greening criteria must be met.
Where more than 30 ha of land is planted, at least three crops must be
grown and the two main crops cannot cover more than 95% of the
land (Defra, 2014a). Greening also requires that 5% of land is in Ecolog-
ical Focus Areas (e.g. buffer strips around fields,hedges).Themodelas-
sumes that the farm already meets these criteria as they are unlikely to
interact with the cultivation method used by the farmer. Constraints
were added to limit the two main crops to no more than 380 ha; thus
at least three crops had to be grown. Note that the rotational constraints
ensure that the model selects crops in proportionsthat are agronomical-
ly appropriate (e.g. first wheat can only follow a break crop).
2.7. Price sensitivity scenario
After running MEETA using the default (2011) price values to pro-
vide direct comparison with the results from Glithero et al. (2012),
the model was run with prices updated to a recently observed lower
commodity price market environment (2014). The rotational con-
straints of the ‘Greening’scenario were maintained for this scenario.
Diesel costs were assumed to be unchanged. Fertiliser prices were
taken from (ABC, 2014): the 2014 N price of £750 t
−1
N is 20% lower
and the 2014 phosphorous price of £620 t
−1
P
2
O
5
is 55% greater than
the default values. Potash price for 2014 was similar (2% lower) to
that used in the original model. Chemical costs are the authors' own
calculations based on the overall crop protection costs for crop types
from ABC (2011) compared with ABC (2014;Table 4). Crop prices
were generally lower in 2014 than in 2011 (Table 5).
NMs were also calculated for 2014 using recent machinery costs,
work-rates and labour wages (£10.19 h
−1
)fromABC (2014). Contrac-
tor fees were based on machinery costs assuming a ‘high’usage rate, a
25% overhead and a 35% surcharge on the labour rate (£13.76 h
−1
).
Use of more recent data has a relatively small impact, although costs
of processing straw (‘baling’) increase by circa 17% (Table 6).
3. Results
3.1. Baseline scenario
Replacing a plough-based CT system with RT changes the optimal
crop mix when maximising profit and NE but for minimised GHG emis-
sions the model maintains the same crop mix (Table 7). For optimised
profit, introducing RT based on RP, the system favours increasing
WOSR area (to half the rotation) whilst increasing WW slightly, at the
expense of WB. WOSR is established after RT practices, whereas the
WW and WB are established after ploughing under the RP scenario. Sys-
tems where all tillage is RT (i.e. no ploughing) favour increased areas of
WW and WOSR over WB, reflecting the increase in available resources
and different timings of operations for WB relative to WW and WOSR,
with WB providing earlier harvesting than WW and thus spreading
work load over a longer period.
Table 3
Embedded energy and GHG emissions for the RT machinery.The equipment weights weretaken as an average of three or more differenttypes of each piece of machinery, drawn from a
range of industry sources. Based on Glithero et al. (2012), the machineryis assumed to be made of steeland embedded energy andemissions per kg of steel areassumed to be 23 GJ kg
−1
and 1.56 kg CO
2
-eq kg
−1
. The lifespan is assumed to be 3000 h.
Machine Weight (kg) Indirect energy (MJ h
−1
) Indirect emissions (kg CO
2
-eq h
−1
)
One-pass cultivator (4.5 m) 7350 56.35 3.83
Medium disc harrow (2–3 m) 1720 13.19 0.90
Spring-tine harrow (6 m) 3500 26.83 1.81
Table 4
Pesticide costs and number of applications per crop. Calculated using the original calcula-
tions presented in Glithero et al. (2012) using prices from ABC (2014).
Crop Pesticide category No. of
sprays
Original price
(£ ha
−1
)
New price
(£ ha
−1
)
Difference
(%)
WW Fungicides 3 68.95 77.93 + 13.0
Herbicides 3 36.01 44.04 +22.3
Growth regulators 2 22.54 23.50 +4.3
Insecticides 1 5.80 4.96 −14.5
Seed treatments and
molluscicides
a
1 14.19 14.81 + 4.4
Seed treatments and
molluscicides
b
1 16.09 16.79 + 4.3
WB Fungicides 2 45.97 51.95 + 13.0
Herbicides 2 24.01 29.36 +22.3
Growth regulators 1 11.27 11.75 +4.3
Insecticides 1 5.80 4.96 −14.5
Seed treatments and
molluscicides
1 13.72 14.32 + 4.4
SB Fungicides 2 45.97 51.95 + 13.0
Herbicides 2 24.01 29.36 +22.3
Seed treatments and
molluscicides
1 15.61 16.29 + 4.4
WOSR Fungicides 2 29.14 22.13 −24.1
Herbicides 3 89.43 80.36 −10.1
Insecticides 2 12.87 11.50 −10.6
Seed treatments and
molluscicides
2 20.66 24.50 + 18.6
WFB Fungicides 2 37.01 30.33 −18.0
Herbicides 2 64.93 73.33 +12.9
Insecticides 2 12.87 13.25 +3.0
a
For a first winter wheat.
b
For a second or continuous winter wheat.
94 T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
For optimised NE, the RP and full RT systems (SRT1, SRT2, DRT and
ZT) still favour half WW with straw harvested but WOSR is now
favoured over WFB as the break crop; this is due to the WOSR crop
being broadcast from the tillage machinery under these full RT systems,
which avoids having a separate seed drilling operation (in the model,
WFB is drilled). As with CT, the optimal crop mix for minimised GHG
emissions for the RP and full RT systems has WW, with the minimum
N fertiliser level and WFB in equal proportions.
For all tillage systems, GHG emissions vary substantially across the
three objectives, largely in response to the amount of purchased N used;
differences in profitability and NE are relatively small in comparison.
GMs were 14–25% greater for the RT systems compared with the CT
system. The slow work-rate for the power harrow in CT results in time
and labour requirements exceeding the available farm resources and,
therefore, contractors are required to complete the process. The cost
of the contractors adds approximately £85 ha
−1
onto the costs of the
CT system compared with the full RT systems (Table 8). These are addi-
tional to the contractor fees incurred by all tillage systems for the use of
the baler and swather.
Although the RP system only has 50% RT, the GMs were much great-
er than for the CT system: lower time and labour requirements for land
preparation free up sufficient resources on farm to conduct these
operations and, therefore, contractor requirements are much lower, sig-
nificantly reducingcosts. The full RT systems have similar GMs; interest-
ingly, DRT has an almost identical GM to SRT1. Although SRT1 requires a
smaller tractor, the work-rate is lower negating the benefit of using a
lower-powered machinery input.
Net energy increased for the RT systems. For the ZT system, the
optimised NE crop mix gives a NE value of 7.7% greater than that in
the CT system. For the ZT system, optimising crop mix for maximising
NE leads to only £11 ha
−1
(approximately 1%) foregone compared
with the optimised crop mix for GMs.
When the crop mix was optimised to minimise the GHG metric,
emissions were lower from the RT systems, with the ZT system having
16.4% lower emissions than the CT system. In the ZT system, optimising
GHG emissions leads to a £174 ha
−1
(approximately 19%) reduction in
profitability compared with the optimised crop mix for GMs. These re-
sult from lower fuel use and lower allocation of embedded energy and
GHG emissions in the machinery.
Small changes in the N emission factor has large impacts on overall
emissions because of the large impact of emissions from applied N. For
the maximised GM scenario, increasing N emissions from 1.6% to
approximately 2.5% negates any GHG emission reduction of ZT com-
pared with CT with the standard N emission factor. This difference is
less pronounced for the minimised GHG emission category as overall
N fertiliser application is lower allowing the N emissions factor to be
up to 3.8% before parity is reached with the CT system.
3.2. Net margin scenario
The RP system results in similar machinery costs to the CT
systemeven though machinery use is reduced. Two sets of machinery
are still required —RT machinery anda plough (Table 8). The fullRT sys-
tems have greater NMs resulting from a combination of lower machin-
ery, fuel and labour costs. The NMfor the ZT system is £256ha
−1
greater
than that in the CT system. Although DRT has a similar GM to SRT1, the
NM for SRT1 is £49 ha
−1
greater, reflecting the extra costs associated
with DRT (a large tractor is required). These results indicate that greater
financial benefits are derived from using RT than the GMs suggest. This
is because NMs take into account the benefits of reduced labour and
machinery costs alongside the reduced fuel use.
3.3. Yield penalty scenario
Sensitivity analysis with reduced crop yields for the ZT system
(whilst leaving yields of the CT system unchanged) shows that an over-
all yield reduction of 14.2% is required for the GM of the ZT system to
equate to that of the CT system (Fig. 1). The results presented in Fig. 1
were calculated by reducing crop yields within the model and re-
optimising, repeating until the yield point is obtained, wherein financial
parity with the resultsfrom the CT system (without yield penalties) oc-
curs. The constant linear relationship observed within Fig. 1 derives
from all variable costs being fixed regardless of yield except fuel costs
for grain drying, which are directly proportional to grain yield.
An 8.1% yield reduction is required for the GMs of the RP to equal
those of the CT system. With respect to the NE metric, the ZT system
can incur a 7.0% yield reduction (Fig. 2) before NE is equal to the CT sys-
tem; for RP the figure is 2.2%. GHG emissions were divided bythe output
of the system in kg of crop output; yields have to be 10.7% lower from
the ZT system for the GHG emissions kg
−1
food output to be equal to
those of the CT system (with normal yields; Fig. 3).
Considering the wider impacts of ZT, a yield reduction from ZT will
require additional land to be used to compensate for the yield foregone.
The indifference point where ZT becomes less economically beneficial
than CT is observed at a yield penalty of 14.2%; given this yield reduc-
tion, approximately 24% more land would be required to maintain the
same total food production in both tonnes of food and in terms of
total calories.
3
This disproportionate increase in additional land re-
quired results from the crop mix for ZT increasing the amounts of
WW and WOSR within the optimal rotation, at the expense of WB,
with WOSR having lower yields than WB. Given a yield penalty of
14.2% under the ZT system, maximised for total farm GM, GHG emis-
sions for producing the same amount of food (and calories) as produced
under the CT are greaterin the ZT system than those in CT, negating the
climatechangebenefits observed from ZT at farm level. Specifically, the
total GHG emissions indifference yield point occurs at a 12.8% yield re-
duction; any yield penalty greater than 12.8% negates the farm level
GHG emission savings of ZT.
3.4. Weed control scenario
GMs remain higher for RT systems even when applying additional
herbicides to combat black-grass. In the extreme scenario of applying
Table 5
Crop prices from the original model and new prices reflecting average crop prices from
November 2013 to October 2014.
Crop Original price
(£ tonne
−1
)
New price
(£ tonne
−1
)
Change
(%)
WW (grain)
a
172.36 144.56 −16.1
WW (straw)
b
43.00 43.50 +1.2
WB, SB (grain)
a
164.42 122.64 −25.4
WB, SB (straw)
b
59.00 51.92 −12.0
WOSR
c
374.08 290.49 −22.3
WFB
c
206.67 221.30 +7.1
a
Defra (UK weekly commodity prices, source HGCA).
b
Defra commodity prices: Hay & Straw, England and Wales average prices.
c
Selected feeding-stuffs prices, Great Britain. This data is available from Anon. (2014).
Table 6
Contractors' costs in the 2011 and 2014 MEETA models.
Machinery 2011 contract cost
(£ hr.
−1
)
2014 contract cost
(£ hr.
−1
)
Difference
(%)
Small tractor 25.01 23.90 −4.4
Medium tractor 35.81 35.28 −1.5
Large tractor 50.21 44.61 −11.2
Combine harvester 121.00 123.48 +2.0
Swather 57.00 57.87 +1.5
Baler (round bales) 45.63 53.51 + 17.3
3
When assuming the following nutritional calories in kcalper kg of grain: 3400 (WW),
3540 (WB, SB),3410 (WFB) and 3969 (WOSR, assuming an oil yield of 44.9%). Values tak-
en from USDA (2016).
95T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
£85 ha
−1
for all crops, ZT still has higher GMs than CT, although these
are slightly lower than the RP system (with no additional herbicides).
For the calculation of NMs, the requirement for extra spraying adds an
additional £4.81 ha
−1
to costs from machinery, fuel and labour. Black-
grass control is unlikely to require such high frequency of application
suggesting that RT remains profitable over CT even when there are
weed problems.
One method of controlling weeds is delaying drilling (usually using a
spring-sown crop) to allow time for a stale-seedbed. In the model SB
was given as an option but is not chosen under standard conditions be-
cause its yields are much lower than the autumn-sowncrops. For the ZT
system, SB is only selected when the yield for all other crops is reduced
by over 17% and even then, SB is only introduced on a small amount of
land. To have a significant amount of SB within the rotation, a yield re-
duction of over 20% for all crops other than SB is required (Table 9).
When the ZT system has SB as the cereal within the rotation, the GM
of the ZT system is actually lower than that of the CT system when
growing WB and WW.
3.5. ‘Greening’scenario
The crop mix for the CT and RT systems optimising for GMs is un-
changed butthe optimised crop mix for maximising NE and minimising
GHG emissions requires bringing in an additional crop to meet the pol-
icy requirements (Table 10). Overall, the financial changes are small.
When optimising for minimum GHG emissions, this is the only scenario
to grow SB, probably selected because of its lower N requirements.
Under both the CT and RT systems the Greening requirement leads to
changes in the crop mix for the maximised NE and minimised GHG
emission scenarios.
3.6. Price sensitivity scenario
GMs were approximately 18–25% lower under 2014 market condi-
tions as compared with the baseline scenario due to the general de-
crease in crop prices and an increase in chemical input prices, as well
as the ‘Greening’constraints (Table 11). For the CT system under 2014
market conditions, WFB was favoured over WOSR as the break crop
Table 7
Crop mixes and corresponding gross margins, net energy and GHG emissions for the three optimisation scenarios for each tillage system.
Tillage system
CT RP DRT SRT1 SRT2 ZT
Maximised gross margins
WW (SR, 75% N) 133.33 138.36 186.48 186.48 186.48 186.48
WB (ASR, SR) 133.33 61.64 27.04 27.04 27.04 27.04
WOSR 133.33 200.0 186.48 186.48 186.48 186.48
Gross margins (£ farm
−1
) 285,782 326,522 351,366 351,508 355,175 357,717
Net energy (GJ farm
−1
) 25,727 26,211 27,668 27,684 27,910 28,067
GHG emissions (kg CO
2
-eq farm
−1
) 1,767,137 1,679,049 1,600,597 1,599,274 1,579,982 1,566,522
Maximised net energy
WW (SR, 75% N) 200.00 200.00 200.00 200.00 200.00 200.00
WOSR –200.00 200.00 200.00 200.00 200.00
WFB 200.00 –––––
Gross margins (£ farm
−1
) 268,172 320,719 347,016 336,757 350,902 353,440
Net energy (GJ farm
−1
) 268,172 26,947 27,771 27,788 28,013 28,161
GHG emissions (kg CO
2
-eq farm
−1
) 935,308 1,662,348 1,591,680 1,590,358 1,571,066 1,558,385
Minimised GHG emissions
WOSR –200.00 ––––
WW (50% N) 200.00 200.00 200.00 200.00 200.00 200.00
WFB 200.00 –200.00 200.00 200.00 200.00
Gross margins (£ farm
−1
) 242,188 256,025 282,011 280,080 283,747 288,229
Net energy (GJ farm
−1
) 20,937 21,060 22,007 21,892 22,117 22,402
GHG emissions (kg CO
2
-eq farm
−1
) 764,305 753,759 672,540 682,424 663,132 638,920
Key: SR —straw removed; ASR —grown after the previous crop had straw removed; % N —percentage of nitrogenous fertiliser applied relative to recommended levels.
Table 8
Fuel, labour, machinery, and contractor costsand the resultant net margins for eachtillage
system for crop mixes under the gross margin maximisation objective.
Tillage system
CT RP DRT SRT1 SRT2 ZT
GM (£ ha
−1
) 714 816 879 888 878 894
Fuel use (L farm
−1
) 91,951 71,193 48,515 48,294 42,605 38,661
Contractors' fees
(£ farm
−1
)
43,748 11,364 9689 9689 9689 9689
Machinery costs
(£ farm
−1
)
118,591 114,786 110,735 89,743 86,058 82,545
Machinery costs
(£ ha
−1
)
296 287 277 224 215 206
Fuel costs (£ farm
−1
) 59,262 45,884 31,268 31,126 27,459 24,917
Fuel costs (£ ha
−1
) 148 115 78 78 69 62
Labour costs
(£ farm
−1
)
27,432 23,484 16,644 16,536 18,804 14,964
Labour costs (£ ha
−1
)695942414737
Net margins
(£ farm
−1
)
172,712 211,625 240,640 260,490 270,222 275,045
Net margins (£ ha
−1
) 432 529 602 651 676 688 Fig. 1. Grossmargins per farm for theZT system with crop yield penalties (solid line). The
dashed line represents the GM per hectare for the CT system without yield penalties.
96 T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
due to the lower price for WOSR and a higher price for WFB. Under
these prices, the crop mix for the full RT systems changes to 45% WW
(with straw baled) and 50% WFB, with 5% WB to fulfilthe‘Greening’re-
quirements. Interestingly, the optimised crop mix for maximum profitis
the same crop mix for maximum NE and it is also very similar to the
crop mix for minimum GHGemissions when both are under the ‘Green-
ing’constraints; the only difference for the latter is that straw is not col-
lected and the minimum N fertiliser rate is applied to WW.
The RT systems bring in WW to 50% of the rotation whilst replacing
WB with WOSR as the third crop because of less pressure on resources.
Whereas RP, DRT and ZT have only 20 ha for WOSR, the SRT systems
have a greater amountof WOSR; this is due to anequal number of tillage
passes for WFB as WOSR whereas for other tillage systems, WFB has
fewer tillage passes, providing a benefit for WFB. Constraining WOSR
to only 20 ha to force the same crop mix only reduces GM by £80 at
the farm-level suggesting minor changes in tillage requirements can
shift crop mix quite a lot. Having extra WOSR results in there being
higher GHG emissions for the SRT and RP systems compared with CT
as WOSR requires N fertilisers whereas WFB does not. For profit
maximisation with the 2014 market conditions, the GM for ZT is ap-
proximately 19% higher than CT, whereas under the 2011market condi-
tions, the GM of ZT was 25% higher than CT.
4. Discussion
4.1. Metrics of sustainable intensification
By reducing tillage intensity farmers can increase their GMs and NE
per hectare whilst lowering GHG emissions. Previous authors have
identified crop yield reductions from RT systems in comparison to CT
systems yet the benefits of RT systems are still observed when taking
into account potential yield reductions. These yield reductions cited in
the literature have typically been modest (less than 4.5% in magnitude
for SRT as reported by Van den Putte et al., 2010); however, the yield
penalties tend to increase with decreasing tillage intensity, so for ZT sys-
tems yield penalties can be higher. By contrast, the yield threshold test-
ing approach presented here has identified that more substantial yield
penalties from RT can be incurred, whilst still achieving a greater finan-
cial return than CT systems. Where RT systems lead to additional crop
protection inputs, in particular for control of weeds such as black-
grass, model results indicate that increased crop protection costs are
not a large barrier to the financial viability of RT systems. This is in
line with other studies (e.g. Vozka, 2007).
Reducing tillage intensity lowers fuel use resulting in increased NE
and GM outputs, and decreased GHG emissions compared with CT.
Fuel use reductions ranged from 23% for RP to 58% for the ZT system,
which is towards the maximum fuel savings suggested by the SoCo
Fig. 2. Net energy per farm for the ZT system with crop yield penalties (solid line). The
dashed line represents the net energy for the CT system without yield penalties.
Fig. 3. GHG emissions per tonne output pe r farm for the ZT syst em with crop yiel d
penalties (solid line). The dashed line represents the GHG emissions per tonne output
for the CT system without yield penalties.
Table 9
Selected crop mix for maximised gross margins when all crops excluding spring barley
incur a 20% yield penalty.
Gross margin maximised
Crop mix
WW (SR, 75% N) 133.33
WB (ASR, SR) 30.68
WOSR 133.33
SB 102.65
Gross margins (£ farm
−1
) 254,247
Net Energy (GJ farm
−1
) 21,868
GHG emissions (kg CO
2
-eq farm
−1
) 1,483,700
Key: SR —strawremoved; ASR —grown afterthe previous crophad straw removed; % N —
percentage of nitrogenous fertiliser applied relative to recommended levels.
Table 10
Optimal crop mixes for maximising GMs and net energy, and minimising GHG emissions
when taking account of Greening requirements.
Tillage system
CT ZT
Maximised gross margins
WW (SR, 75% N) 133.33 186.48
WB (ASR, SR) 133.33 27.04
WOSR 133.33 186.48
Gross margins (£ farm
−1
) 285,782 357,717
Net energy (GJ farm
−1
) 25,727 28,067
GHG emissions (kg CO
2
-eq farm
−1
) 1,767,137 1,566,522
Maximised net energy
WW (SR, 75% N) 200.00 190.00
WB (ASR, SR) –20.00
WOSR 20.00 190.00
WFB 180.00 –
Gross margins (£ farm
−1
) 270,687 354,025
Net energy (GJ farm
−1
) 26,142 28,122
GHG emissions (kg CO
2
-eq farm
−1
) 101,633 1,561,771
Minimised GHG emissions
WW(50% N) 180.00 180.00
WFB 200.00 200.00
SB 20.00 20.00
Gross margins (£ farm
−1
) 238,683 281,251
Net energy (GJ farm
−1
) 20,559 21,988
GHG emissions (kg CO
2
-eq farm
−1
) 764,569 642,223
Key: SR —strawremoved; ASR —grown afterthe previous crophad straw removed; % N —
percentage of nitrogenous fertiliser applied relative to recommended levels.
97T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
Project Team (2009) in their review of the literature. The increased NE
and decreased GHG emissions also result from reduced machinery use
of the RT systems modelled and lead to lower embedded energy within
machinery held on farm and hence lower emissions allocated to the
farm systems.
Cost savings from the RT systems result from lower fuel inputs,
lower contractor requirements, as well as the flexibility to grow a great-
er area of more valuable crops in the rotation, thus providing additional
revenue. For CT, time and labour constraints during the soil cultivation
period force the crop mix to include a third of the land as WB, a less
valuable crop, to spread the workload and limit contractors' costs. As
the utilisation of RT reduces cultivation time, a greater area of the
more valuable crops, WW and WOSR, can be grown instead.
Cost savings represent a key driver of RT uptake in Northern Europe
(Morris et al., 2010) and our results demonstrate substantial cost sav-
ings from the adoption of RT systems. In terms of current RT use, in En-
gland RT practices cover approximately 30–40% of arable land but these
tend to be used in conjunction with ploughing. Townsend et al. (2016)
defined mixed tillage systems as using both ploughing and RT practices
within the rotation. These range from the use of ploughing at a specific
place within the rotation (i.e. rotational ploughing, such as using it for
the non-break crops in the current model) through to using it dynami-
cally in response to specific conditions (i.e. strategic tillage). Only a
small proportion of farms solely used RT and very few farms used ZT.
RP is commonly used because it allows farmers to gain some benefits
from using RT whilst minimising the risk of yield penalties. In the cur-
rent model RP had a more favourable GM than the reduction in fuel
costs would suggest as the reduced machinery requirement meant
that operations could be completed without exceeding farm resources,
thus avoiding the need to use contractors for cultivation. This demon-
strates that a partial reduction in ploughing can have relatively large fi-
nancial benefits.
The model results show clear benefits of using RT over CT but there
were only relatively minor differences in the GMs of the different full RT
practices. The NMs show that greater benefits are found with lower in-
tensity RT, demonstrating that NMs are a better means of establishing
the value from changes to cultivation practices as they provide a more
holistic financial metric. Even so, the financial benefits of ZT over shal-
low RT are relatively minor, even without taking account of potential
yield penalties. Verch et al. (2009), in conducting a field experiment
comparing tillage systems, found that in 17 out of 20 comparisons, fi-
nancial returns did not significantly differ between ZT and SRT systems.
The ZT treatment did suffer yield penalties resulting in the lower profit-
ability; the authors suggest that over a longer time period the yield gap
between SRT and ZT treatments would decrease, giving ZT a better prof-
itability. Vozka (2007) found that ZT had a similar cost to SRT when ZT
incurred additional herbicide costs.The model results show that a finan-
cial incentive to move away from plough-based agriculture exists, but
that additional financial benefits from reducing tillage intensity further
are fairly limited. This helps to explain why RT practices tend to remain
quite intensive (based on the average depth of RT practices; Townsend
et al., 2016) and ZT is relatively rare. Davies and Finney (2002) sug-
gested that the risks of yield losses would also restrict the use of ZT
and SRT.
Our results demonstrate that input and output price variability can
lead to contrasting financially optimal crop mixes. For example, the in-
crease in GM for ZT over CT were 25% in the original model but 19% with
the lower 2014 prices; this is due to the optimal crop mix with lower
prices favouring WFB, which has lower tillage requirements under the
CT scenario and, therefore, the cost savings from moving to a RT system
are lower than a rotation favouring WOSR. Together with the farm-
specific factors, this strongly suggests that specific conditions on the
farm, and with respect to market prices, could determine the extent of
benefits from moving to a RT system from CT.
The model only captured one metric of sustainability (GHG emis-
sions). RT practices are associated with other environmental benefits
that were not captured by the model such as reducing water pollution
resulting from leaching and run-off of pesticides, nutrients and soil sed-
iment (Holland, 2004). There is also considerable uncertainty regarding
the environmental benefits of RT as these tend to be very site-specific
with a strong influence from factors such as climate, soil-type and
topography. Capturing these for a model like MEETA is not possible
based on the limited data that is available, and is beyond the scope of
this current study.
As shown by Gibbons et al. (2006), uncertainty is also present in
GHG emission estimates. As noted in our methodology here, the emis-
sions of N
2
O are highly variable but tend to be initially higher for ZT sys-
tems. As the model results are sensitive to the N
2
O emission factor, this
Table 11
Optimised GMs, NE and GHG emissions for the different tillage systems for the 2014 price scenario with the ‘Greening’requirement.
Tillage system
CT RP DRT SRT1 SRT2 ZT
Maximised gross margins
WW (SR, 75% N) 180.00 200.00 200.00 200.00 200.00 200.00
WB (ASR, SR) 20.00 –––––
WOSR –20.00 20.00 81.89 81.89 20.00
WFB 200.00 180.00 180.00 118.11 118.11 180.00
Gross margins (£ farm
−1
) 233,006 245,692 270,501 268,858 272,524 276,716
Net energy (GJ farm
−1
) 25,962 26,335 27,283 27,390 27,615 27,677
GHG emissions (kg CO
2
-eq farm
−1
) 937,198 999,608 918,356 1,155,161 1,135,869 884,768
Maximised net energy
WW (SR, 75% N) 200.00 200.00 190.00 190.00 190.00 190.00
WB (ASR, SR) ––20.00 20.00 20.00 20.00
WOSR 20.00 180.00 190.00 190.00 190.00 190.00
WFB 180.00 20.00 ––––
Gross margins (£ farm
−1
) 230,595 233,501 258,331 254,329 257,995 262,436
Net energy (GJ farm
−1
) 26,142 26,766 27,726 27,742 27,967 28,122
GHG emissions (kg CO
2
-eq farm
−1
) 1,016,233 1,598,378 1,595,643 1,594,321 1,575,029 1,561,771
Minimised GHG emissions
WW (50% N) 180.00 180.00 180.00 180.00 180.00 180.00
WFB 200.00 200.00 200.00 200.00 200.00 200.00
SB 20.00 20.00 20.00 20.00 20.00 20.00
Gross margins (£ farm
−1
) 204,298 216,816 238,475 236,545 240,211 244,691
Net energy (GJ farm
−1
) 20,559 20,681 21,593 21,478 21,703 21,988
GHG emissions (kg CO
2
-eq farm
−1
) 764,569 754,022 675,842 685,726 666,434 642,223
Key: SR —straw removed; ASR —grown after the previous crop had straw removed; % N —percentage of nitrogenous fertiliser applied relative to recommended levels.
98 T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
could mean the GHG emission savings are over-estimated. In contrast,
greater emissionssavings for ZT have been suggested through increased
carbon (C) sequestration in the soil (Powlson et al., 2012). Sørensen
et al. (2014) included changes in soil C in their calculation of net chang-
es in GHG emissions resulting from switching to RT and this accounted
for larger GHG emission reductions than from reduced machinery use.
In the literature there is considerable uncertainty regarding this and it
is disputed whether there is actually an increase in C from switching
from CT to ZT; although soil C increases in the upper layers of soil,
there is a decrease deeper in thesoil, and thus ZT maynot lead to greater
C sequestration (Baker et al., 2007; Soane et al., 2012).
The SI concept is broadly based upon increased food production
whilst impacts on the environment are either held constant or reduced.
If RT practices lead to a yield penalty, would these practices still count as
SI? The benefit of the system-based approach taken in the MEETA
model is that it highlights that although the model can financially sup-
port a set amount of yield loss, leading to reduced GHG emissions at
the farm-level, the wider GHG emissions when examined as a function
of total food or calorie production are actually negated given this loss of
yield. Hence, the corollary of farm-level SI practices derived from the
implementation of RT maybe negative environmental outcomes global-
ly. Specifically, given an approximate 13% yield reduction under ZT, local
GHG emissions would be reduced,whilst globally, there wouldbe an in-
crease in GHG emissions to maintain total calorie production.
Even in the absence of a yield penalty, introducing RT leads to lower
overall food production because RT practices reduce pressure on re-
sources allowing the amount of WOSR to be increased in the crop mix,
resulting in lower overall calorie production from the farm. This high-
lights that under free market conditions, farmers respond to price sig-
nals to maximise net financial return and as a consequence do not
necessarily maximise overall food production. Within the framework
presented in this study, the model assumes that farmers operate in a
global market, and that their individual crop mix choice does not influ-
ence the market price.
4.2. Policy and practice
The change in the optimal crop mixdemonstrates potential opportu-
nities to change crop rotations when switching from CT to RT because of
quicker field preparation. This has important implications for land-use
management. In the model this has resulted in a less diverse crop mix
in terms of a much smaller area of WB. Crop rotations are important
to reduce pests and disease whilst maintaining or increasing soil pro-
ductivity (Martin et al., 2006). The ‘Greening’requirement discourages
the growing of monocultures by requiring at least three crops to be
grown to claim the full Basic Payment Scheme subsidy (Defra, 2014a);
however, in practice, as we have modelled, a large proportion of the
area can be in two crops: for example, the optimal crop mix under the
maximum GM scenario would still be allowed under the ‘Greening’re-
quirements even though only a small area of WB is grown. The optimal
crop mixes for maximising NE and minimising GHG emissions would
not be allowed as there are only two crops; however, meeting the
requirements would only have a marginal effect as only 20 ha (5%)
would need to be replaced with an alternative crop.
Although RT provides extra flexibility in rotations for farmers by
freeing up resources, it typically restricts the use of root crops, which
are less suitable for RT practices. A standard risk management strategy
that farmers adopt to address price and yield variability is to have a
greater number of crops in their rotation (Hardaker et al., 2004); if RT
systems reduce the flexibility of crop choices they will be less attractive
to risk averse farmers. Utilising a mixed tillage system would provide
farmers with the widest range of crops by reducing resource pressure
whilst allowing tillage practices to optimally fit each crop grown.
Townsend et al. (2016) found that the use of RT varied greatly with
crop type, being extensively used for WOSR but only very rarely for
root crops (potatoes, sugar beet) and field beans. The extent of RT
usage thus partly depends on the crops grown.
Control of black-grass represents a major current and potential
threat to arable cropping systems in the UK. Although the costs of her-
bicides to enable RT are not prohibitive to the use of RT, it may be that
an additional herbicide application is insufficient to control weeds
such as black-grass, especially when the weed burden has established
itself. Ploughing is one method of controlling black-grass and the
model considered theuse of a mixed tillage system (i.e. RP). The results
show that it is possible to incorporate RT with inversion, plough-based
cultivations, and still achieve environmental and financial benefits.
However, for continuous RT, alternate strategies are required. There
is evidence to support the use of stale-seedbeds using later-sown crops
to reduce black-grass (Lutman et al., 2013). The quicker cultivation of
land leaves a potentially larger time window in the autumn for a stale
seedbed prior to autumn-sown crops, although delayed sowing can re-
duce yield (Hay and Walker, 1989). Inclusion of spring-sown crops
within the rotation would allow a considerably longer period of stale
seedbed and more effective means of controlling weeds (Chauvel
et al., 2009). UK crop rotations tend to be dominated by WW and
WOSR but increasing rotation length and diversity, such as by the
inclusion of spring-sown crops, would reduce reliance on herbicides
(Ferguson and Evans, 2010). However, incorporating other crops into
the rotation could reduce profitability. In particular, spring-sown crops
are generally financially unattractive to farmers (Pardo et al., 2010)
and in cereal rotations are less common than autumn-sown crops.
There is very little spring-sown wheat or oilseed rape in the UK and al-
though there is more spring-sown barley than winter-sown barley,
winter-sown barley tends to dominate in the south and east whilst
spring-sown barley tends to dominate in the west and north (ABC,
2014; Defra, 2014b). This observed cropping pattern is reflected in the
current model whichdoes not select spring-sown crops, even when au-
tumn sown crops necessitate bringing in contractors to prepare land for
autumn-sown crops. Inclusion of SB in the ZT system negated the cost
savings from lowering tillage intensity. With a potential reduction in
the efficacy of herbicides and stricter legislation on use of crop protec-
tion chemicals, practices such as more diverse rotations and stale seed-
beds may assume greater importance in the battle to control weeds.
An important caveat for the model results is that the benefits of RT de-
pend on the baseline conditions from which measurement of these tillage
changes takes place. The model considered quite an intensive CT system
and the soil was “heavy”, giving slower work-rates. Thus, there was pres-
sure on farm resources necessitating the use of contractors. Where time
and labour is less constrained during the cultivation period less benefit
is likely to be seen from utilising RT. The CT system in the model could
forego the power harrow and use a ‘soil packer’instead to break up soil
clods, which would reduce costs whilst still maintaining ploughing as
an option; this might be seen as more attractive to a farmer than bringing
in RT, although this will depend on the relative performance of crops
under each system. Soil type has an influence on the feasibility of RT
(Davies and Finney, 2002; Morris et al., 2010) but also on the relative ben-
efits where heavy soils, which have lower work-rates, have relative great-
er fuel savings (SoCo Project Team, 2009).
Given the possible environmental benefits of RT and the financial
benefits previously noted, it is worth exploring the reasons why more
land is not currently under RT. Townsend et al. (2016) presented
some of the barriers preventing greater use of RT and some of these
have been demonstrated in the MEETA model results.
There are a multitude of factors influencing the adoption of soil con-
servation practises in agriculture (Knowler and Bradshaw, 2007; Prager
and Posthumus, 2010). Studies show inconsistency in the factors they
identify as influencing adoption, which is partly due to factors tending
to be site-specific and partly because of the variety of methodologies
used to make assessments (Knowler and Bradshaw, 2007)—behaviour
regarding adoption has been determined by personal, socio-cultural,
economic, institutional and environmental approaches. Despite this
99T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
variety, financial benefits are frequently identified as the key driver of
whether or not soil conservation practices are adopted (Prager and
Posthumus, 2010).
4.3. Machinery ownership
Machinery costs influence adoption of RT practices. When moving
from CT to mixed tillage the requirement to hold machinery suitable
for both CT and RT simultaneously will typically leadto greater machin-
ery depreciation costs than would result from a single tillage system ap-
proach. This is demonstrated in the similar machinery costs between
the CT and the RP systems. It is possible to have a RP system without
the expense of additional equipment as farmers can directly broadcast
seed of crops such as WOSR into the stubble of the previous crop, thus
avoiding the expense of two sets of tillage equipment. Results from
the MEETA model presented in Glithero et al. (2015) identified that de-
cision making with respect to levels of machinery ownership was a key
financial driver in determining optimal crop mix. In the MEETA model
results, the SRT and ZT systems did not require the large tractor,
which is partly responsible for the RT systems having much higher
NMs. In contrast, the DRT system requires a powerfultractor, which ac-
counts for the DRT system having lower NMs than SRT1, even though
these two systems produce identical GMs. A key point here is that
gaining the full benefits of switching from a CT system to a RT system
would require a change in the machinery present on the farm: this is
not a cost free process and includes both capital and learning costs. In
the MEETA model the learning costs were not covered by the NMs but
could have included a slower work-rate initially. As mentioned in
Townsend et al. (2016) financial grants to facilitate the transition be-
tween CT and RT systems to cover these costs may be required to en-
courage further uptake. However, the benefits are uncertain and thus
a farmer'sattitudeto this risk is also relevant. Farm size is a determinant
of whetherRT practices are likely to be used(Townsend et al., 2016)and
this could be due to larger farms being able to finance much larger trac-
tors and afford multiple sets of tillage equipment when mixed tillage
systems are used.
4.4. Perceptions and penalties
Despite thepotential financial, energy and environmental benefits of
RT, uptake of these practices will be affected by the prior beliefs of
farmers (Andrews et al., 2013), in particular regarding the yield penal-
ties that may be incurred. The model does not capture specific risks as-
sociated with RT practices, such as severe weed problems. As noted,
farmers tend to be risk-averse and it is possible that some farmers are
continuing to use CT as there are strategic risks associated with
switching to RT systems. These risks include yield penalties and weed
problems but also extend to the problem of adopting a new technology
that may not be suitable for a farmer's cropping system. The farmer's
subjective probability of the effect of moving from CT to RT will also af-
fect uptake with the assumptions about increased risks. These may be
based on experience; RT was common in the UK in the 1970s but its
use declined due to difficulties with increased weed burden (Davies
and Finney, 2002). If farmers had experienced this they may be unwill-
ing to try RT again, even though better equipment (e.g. seed drills) is
available and there is greater knowledge of best practices.
Farmers' reluctance to adopt of RT based upon concerns about yield
penalties are understandable given the paucity of data on yield impacts
from the use of RT, in particular after the ban on burning stubble. On av-
erage, evidence suggests that yields are slightly lower under RT and this
might discourage farmers from adopting RT. Yet under certain condi-
tions there is the potential for RT systems to result in greater yields
(Knight, 2004; Verch et al., 2009). One aspect potentially not accounted
for in field experiments that compare RT systems is the commercial po-
tential to drill crops earlier, as RT practices require less field preparation
time per hectare than CT. The improved timeliness of field operations
resultingfrom the lower labour and machinery requirements of the sys-
tem could lead to better yields. Determining how yield reacts to certain
soil types, climate, timeliness of crop establishment and cropping sys-
tems wouldprovide better information forfarmers to assist them in de-
cisions regarding RT; however, as argued by Davies and Finney (2002)
long-term yield experiments are expensive, time-consuming and par-
ticularly site-specific. Considering the results of the current study, RT
systems generate greater GMs and NMs even when accounting for
yield penalties and this suggests farmers should not focus on potential
yield reductions but also consider cost savings, and hence margins,
when making decisions about tillage practices.
Adoption of RT is also limited by farm-specific factors such as farm
size, crop rotation,machinery available, climate, soil type and weed bur-
den. As shown by Ogle et al. (2012), in the US, RT can have beneficial
yield effects in drier, warmer climate conditions and is thus more suited
to areas with these conditions. In the UK, where water-stress is less
common, there is likely to be less incentive to adopt RT.
4.5. The future for reduced tillage
In combination, the above factors may go some way to explaining why
only 30–40% of arable land in England is currently under RT. It is also pos-
sible that farmers are identifying that there are greater risks associated
with low intensity RT (SRT and ZT) and are either using deeper reduced
tillage or, where they are using SRT or ZT, also using rotational ploughing.
Although there is some financial encouragement for soil conserva-
tion practices in agriculture through the CAP, in general farmers are
not rewarded for the positive externalities associated with the adoption
of these practices. It has been suggested that farmers should be
incentivised to use RT. For example, reducing tillage intensity has been
suggested as a way of sequestering carbon in the soil (Powlson et al.,
2012) and providing carbon credits for farmers using RT practices
would encourage uptake (Alexander et al., 2015). However, there is
no robust evidence that RT leads to increased soil C stocks; furthermore,
as discussed above, there is still much uncertainty generally over the ef-
fectiveness of different interventions on greenhouse gas emissions.
This uncertainty extends to farmers' perceptions of RT: it has been
said that UK farmers “regard soil conservation practices with suspicion
as they perceive a great uncertainty on their effectiveness and impact
on farm productivity”(Prager and Posthumus, 2010). This would
seem to be a rational response given current levels of understanding.
Davies and Finney (2002) emphasise the variability in the soil impacts
found in RT field experiments and the uncertainty regarding the
scaling-up of these impacts from field experiment to farm level.
Townsend et al. (2016) question whether the current RT practices
(deep tillage used in mixed tillage systems) provide the environmental
benefits presented in the literature.
In reality, RT is partof a suite of soil conservation practices that could
be used to improve the sustainability of agriculture. Alongside longer
rotations and permanent soil cover RT is referred to asconservation ag-
riculture and these practices complement each other and enhance the
benefits derived from each practice individually. Incentivising the up-
take of various components of conservation agriculture, such as longer
rotations, could be operationalised through modified ‘Greening’re-
quirements and better soil practices implemented through the Soil Pro-
tection Review. Such practices would, on the evidence presented herein,
represent a positive step towards increasing the sustainable intensifica-
tion of agricultural system, enhancing the financial, environmental and
energy outcomes from primary food production systems.
5. Conclusions
Reducing tillage intensity increased farm-level gross margins and
net energy potential whilst lowering greenhouse gas emissions. Model-
ling to include flexibility in labour and machinery gave greater financial
benefits as measured by net margin and lowers emissions still further.
100 T.J. Townsend et al. / Agricultural Systems 146 (2016) 91–102
Given the relatively large threshold yield reductions that are required
before RT systems are less financially attractive at the farm level than
CT, this suggests that RT is one route towards sustainable intensification
(SI). However, given that yield reductions will require an increased use
of land to compensate for these yield penalties, the locally observed en-
vironmental benefits from RT may be negated when examined globally.
The modelling framework within which these results were generated
also allows us to quantify the farm-level impact of financial, energy
and environmental metrics associated with this potential SI practice.
Reduced tillage both increases and reduces crop choice flexibility; flex-
ibility is increased by reducing work-rates for field preparation but re-
duced by preventing the growing of crops unsuited to RT. Mixed
tillage systems offer the greatest flexibility, but may compromise some
of the environmental benefits. Despite the potential financial benefits, up-
take of RT is still relatively low at the time of writing, at 30–40% of the ar-
able land area of England. There are a range of reasons that can explain
this, including farmers' risk attitudes and perceptions and the benefits,
particularly for weed control, of systems that include some ploughing.
With better quantification of the GHG benefits and increased emphasis
on ‘Greening’the CAP, there is considerable scope for reconfiguring
existing policy mechanisms to encourage greater uptake of a range of
RT approaches.
Acknowledgements
The research reported here was funded in part by the Biotechnology
and Biological Sciences Research Council(BBSRC) Sustainable Bioenergy
Centre (BSBEC), under the programme for ‘Lignocellulosic Conversion
to Ethanol’(LACE) [Grant Ref: BB/G01616X/1], and from the Defra (UK
Government) SIP Projects LM0201 ‘Sustainable Intensification Platform
Research Project 1 —Integrated Farm Management for Improved Eco-
nomic, Environmental and SocialPerformance’and ‘Sustainable Intensi-
fication Platform Research Project 2 —LM0302 ‘Opportunities and Risks
for Farming and the Environment at Landscape Scales'.
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