cultivation and soil organic matter management in low input cereal production following the...

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doi:10.1016/j.biosystemseng.2004.07.003SW—Soil and Water

Biosystems Engineering (2005) 90 (1), 85–101

Cultivation and Soil Organic Matter Management in Low Input Cereal Productionfollowing the Ploughing out of Grass Leys

M.B. McGechan; J.K. Henshall; A.J.A. Vinten

Land Economy Research Group, Research Division, SAC, Bush Estate, Penicuik EH26 0PH, UK;e-mail of corresponding author: [email protected]

(Received 11 December 2003; accepted in revised form 26 July 2004; published online 7 December 2004)

Nitrogen fertility of low input cereal systems following ploughing out of grass leys is investigated using the soilnitrogen dynamics model SOILN with its linked cereal growth model, supported by a set of experimental datafor spring and winter barley cropping. Soil organic matter (OM) plays an important part in transfer of fertilityfrom the fertility-building ley phase to the fertility exploitation arable phase of ley-arable rotations. One aspectinvestigated is a possible role of ‘protected organic matter’, a component of soil OM which generallymineralises at a slow rate, but which becomes ‘unprotected’ so it mineralises at a somewhat faster rate for alimited period following disturbance of the soil by ploughing. Measurements of CO2 emissions in thelaboratory and field support the concept of protected OM. In simulations with the SOILN model, this isrepresented by a temporary increase in the value of the humus decomposition rate constant for a few weeksfollowing ploughing. The assumption of raised humus decomposition rates after ploughing makes someimprovements to fits of simulations with the linked models to measured grain and straw yields and N offtakes,as well as giving a more realistic decline in soil OM over successive years of cereal cropping. Predictivesimulations demonstrate how, in the absence of mineral N fertiliser, cereal crop yields decline over the yearsfollowing ploughing out of leys, but yields can be maintained by making applications of organic manureor slurry.r 2004 Silsoe Research Institute. All rights reserved

Published by Elsevier Ltd

1. Introduction

In low input or organic cereal production, plantnutrients must be obtained as far as possible fromorganic sources with little or no mineral fertiliser. Themost critical nutrient is nitrogen (N). In the absence ofmineral fertiliser, N must be supplied to crops fromorganic sources, either N fixed by legume crops ororganic manure, or more often a combination of thetwo. Low input and organic cereals are commonlygrown in a rotation with grass/clover leys. The ley phaseof the rotation is regarded as a fertility building periodand the arable phase is regarded as the fertilityexploitation period. Nitrogen fixation depends on theclover in the ley mixture, but even a pure grass leyreceiving organic manure or slurry can incur asignificant build of organic N fertility over a long

1537-5110/$30.00 85

period. The soil organic matter (OM) component of thesoil is a key to transfer of nutrients from that fixed orbuilt up during the ley phase to that utilised during thearable phase. The quantity of N held in this pool risesduring the ley phase and falls during the arable phase.Nitrogen must be mineralised to inorganic forms forcrops to have access to it as a nutrient. Mineralisation ofhumus OM takes place at a slow rate which variesmainly in relation to temperature and soil wetness,factors over which no management controls can beexerted. However, some researchers (e.g. Verberne et al.,1990) have described the concept of ‘protected OM’, acomponent of soil OM which generally mineralises at aslow rate, but which becomes ‘unprotected’ so itmineralises at a somewhat faster rate for a limitedperiod following disturbance of the soil by ploughing.This provides the possibility of limited management

r 2004 Silsoe Research Institute. All rights reserved

Published by Elsevier Ltd

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M.B. MCGECHAN ET AL.86

control over mineralisation so the release of inorganic Ncorresponds more nearly to the crop requirement of Nas a nutrient.

This paper examines the processes by which low inputand organic cereal crops receive their N fertility via soilOM, together with the role of cultivations in managingthe fertilisation process. Use has been made of both datafrom a field trial on spring and winter barley followinggrass, and of a soil N dynamics model.

2. Experimental data

The field and laboratory experiments which provideddata for this study have been described in detail byVinten et al. (2002). The 3 yr field experiment withbarley crops was conducted in 1996–98 near Penicuik,Scotland in a field known as ‘Beechgrove’ which hadpreviously had long-term grazed grass and grass–cloverswards. Effects were examined of timing of cultivations(spring or autumn), ploughing to two depths or zerotillage, and various levels of mineral fertiliser applica-tion (including zero). Measurements were made of cropyields and N uptake, together with sampling of soil,water and gaseous emissions. Parallel laboratory in-cubation experiments were carried out on soil samplesfrom the field to examine decomposition of soil OM andmineralisation of organic N.

3. Model description

3.1. Soil nitrogen dynamics models

There are a number of soil nitrogen dynamics modelsin existence, as reviewed by Wu and McGechan (1998a),Shaffer and Ma (2001) and McGechan and Wu (2001).The SOILN model (Johnsson et al., 1987) has an optionof operating interactively with a crop growth model. Inthis respect, it differs from most other soil nitrogenmodels which assume a fixed pattern of N uptake overthe growth season based on a logistic growth curve. Asdistributed by its Swedish authors, the interactivegrowth sub-model in SOILN is for a cereal crop(Eckersten & Jansson, 1991). Adaptation of the grassor grass/clover growth model originally developed byTopp and Doyle (1996) as an alternative sub-model forSOILN has been described by Wu and McGechan(1998b, 1999) and Wu et al. (1998). In the current study,weather-driven simulations were carried out withSOILN linked to the cereal growth model and calibratedto represent the field experiment. A simulation was alsocarried out with the grass version of the linked model,

to support measured values of soil parameters at thetime of ploughing.

3.2. Details of soil organic matter processes represented

in SOILN

The soil processes simulated by SOILN are describedin detail by Johnsson et al. (1987), Wu and McGechan(1998a) and McGechan and Wu (2001), so are onlysummarised here. In the model, N and carbon (C) areheld in a number of pools, with specified rate parametersfor transformation flows between pools. Organic C andN are contained in a slow cycling pool for soil humus,and two fast cycling pools for plant-derived litter andmanure respectively (Fig. 1). There are also inorganic Npools for ammonium and nitrate. Decomposing C flowsfrom the two fast cycling pools split three ways accordingto the efficiency factors fe and fh, going respectively to theslow cycling humus pool, to released CO2, and tomicrobial biomass which is recycled back to the sourcepool (Fig. 2). Both these efficiency factors fe and fh can beset to a different value for litter or for manure OM.Nitrogen processes fit in around the C decomposition,and whether this takes the form of mineralisation orimmobilisation depends on the current C/N ratios of thesource and destination pools, as well as the values of fe

and fh. Carbon decomposed from the slow cycling humuspool is all released as CO2, and consequently allcorresponding N in this pool is mineralised.

Decomposition of C in the organic pools follows afirst-order exponential decay relationship, with specifiedrate parameters which are adjusted to take account ofsoil temperature and water content. The temperatureadjustment factor et, which is very relevant whencomparing reported rate constant estimates based onexperiments carried out at different temperatures, isgiven by the following expression:

et ¼ QðT�TbÞ=1010 (1)

where: Q10 is the response to a 10 1C soil temperaturechange; T is the actual soil temperature; and Tb is thebase temperature at which the decomposition rateconstant has been measured. A value for Q10 of 2�58was retained from a previous calibration of the SOILNmodel (Wu & McGechan, 1998a). Temperatures andwater contents are derived from simulations with the soilwater and heat model SOIL (see Section 4.4).

3.3. Additional soil organic pools

The SOILN model has a very simple sub-division ofsoil OM into fast and slow cycling pools, with the two

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CO2

PhotosynthesisBiomass Runoff Manure

Leaching

Respiration

Output

Respiration

Humus

Humus

(a)

(b)

Denitrification

Output

Faeces

Uptake

Fertiliser

Deposition

Faeces

Manure

Litter

Mineralisation

Mineralisation

Nitr

ific

atio

n

Mineralisation

HumificationHumification

NO3

NH4

N2O, N2

Litter

Mineralisation and humification

Mineralisation and humification

Fig. 1. Block diagram of (a) carbon cycling and (b) nitrogen cycling in the SOILN model

C litter C humus

(1−fh) feSynthesis of microbial biomass

Humification

CO2

fh fe(1−f

e)

Fig. 2. Carbon flows to and from organic matter pools in theSOILN model: fe and fh are ‘efficiency factors’ indicating the

proportion of the flow that goes by each route

CULTIVATION AND ORGANIC MATTER MANAGEMENT 87

fast cycling pools for materials of different derivationbut both with similar decomposition rates. Organicmatter in each of the fast cycling pools and in the slowcycling pool in SOILN is in effect an assemblage of anumber of sub-component organic materials, each witha different decomposition rate. The specified decom-position rate for the pool is therefore the instantaneousmean of all the components in the pool. However, in

some other soil N dynamics models either the fastcycling or slow cycling pools (or both) is furthersubdivided into slightly slower and slightly faster cyclingsub-pools. This is in addition to a separate pool for OMin microbial biomass which is present in some models(including an option in SOILN which is not used in thecurrent study). Additional pools lead to a complexmodel structure making it difficult to obtain data toaccurately calibrate the decomposition rates for all thecomponents.

3.4. Protected organic matter in soil nitrogen dynamics

models

Verberne et al. (1990) have described a model of soilOM dynamics which has a particular emphasis on theconcept of ‘protection’ of some components. Protected

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OM cycles at a slow rate when the soil is undisturbed, butbecomes unprotected with a much faster cycling ratewhen soil is disturbed by ploughing or other cultivations.The Verberne model is one with a complex structure andmany different categories of OM, some of which arefurther sub-divided into ‘protected OM’ and ‘unprotectedOM’. The structure of the Verberne model was furtherprogrammed and tested to compare the protected poolsstructure with an alternative representation of protectionbased on the kinetics of sorption and desorption(Whitmore et al., 1997; Hassink & Whitmore, 1997).

For the current study, it was not possible with thedata available to calibrate all the OM pools and flowrates in the complex structure of the Verberne model.Instead the simple structure of the SOILN model wasadopted with only two OM pools, a fast cycling litterpool (in effect including six separate pools from theVerberne model) and a slow cycling humus pool(incorporating three separate pools). However, use wasmade of a facility in the SOILN model for makingchanges to one or more specified parameters at certaindates during the course of a simulation. A transfer of theprotected material to an unprotected form (with a fasterdecomposition rate) at ploughing, and back againduring the period following ploughing, was indicatedby a change in the overall decomposition rate for thehumus pool. Since three of the pools in the Verbernemodel were incorporated into the humus pool inSOILN, flows between these individual pools did notneed to be represented. When set up in this way, thischange in overall decomposition rate arises partlybecause of the change between protected and unpro-tected forms of part of the constituent OM, but alsopartly because of the changing quantity of protected/unprotected OM as a proportion of the much largertotal slow cycling humus OM.

4. Model calibration

4.1. Procedure for calibration of decomposition rates

Estimation of decomposition rate parameters iscommonly based on laboratory incubation experimentsin which CO2 release is measured from soil samples heldat a controlled temperature. A previous calibration ofSOILN for two sites near that of the current study (Wuet al., 1998) was based on incubation experimentsdescribed by Vinten et al. (1996), in which soil sampleswere held at temperatures of 23 1C for 204 days, then at12 1C for a further 244 days. Carbon dioxide releaseduring the second period gave an indication of thedecomposition rate for slow cycling humus, while thedecomposition rate for plant litter material was esti-

mated from CO2 release during the first period aftersubtracting that from humus decomposition during thisperiod. The higher temperature over the first period ofthese experiments was intended to reduce the timerequired for near complete decomposition of the littermaterial, so CO2 release during the second period couldbe assumed to be due to decomposition of humus alone.There are also a number of litter and humus decom-position rate parameters for various soils reported inother papers, which have been reviewed and comparedby Wu and McGechan (1998). In the experimentsdescribed in this study, laboratory soil samples takenfrom the field immediately before ploughing and also atthe end of the experiment were incubated for a period of212 days at 12 1C. These were supplemented by CO2

measured in the field for a period following ploughingand establishment of spring barley in 1996, which it washoped would give an indication of the extent to whichdecomposition rates are temporarily raised followingploughing. It was hoped that the combination of CO2

measurements in the laboratory and the field wouldprovide evidence to support (or not) the concept ofprotected OM becoming unprotected after ploughing.

4.2. Adjustments to cereal crop growth model

Parameter values for the cereal growth model asdescribed by Eckersten and Jansson (1991), and furthertested by Wu et al. (1998), were found to give a poorrepresentation of experimentally measured yields and Nofftakes in simulations. Also, the simulated pattern ofgrowth of leaf, stem, grain and root components was notrealistic. Some parameter values, in particular themaximum leaf age and the initiation of grain develop-ment, were adjusted by trial and error to give anappropriate pattern of crop development (Fig. 3).Further adjustments were made to the photosyntheticradiation use efficiency factor and to the maximum Nconcentrations of crop components (specified separatelyfor grain, stems, leaves and roots), to give simulatedyields and N offtakes closer to the measured values(Table 1). A similar parameter adjustment procedurewith the SOILN cereal growth sub-model has beendescribed by Blomback and Eckersten (1998) andBlomback et al. (1998), who chose a number ofalternative values of some parameters according tocircumstances, including different values in each year ofa simulation in some cases. The values selected for thecurrent study (Table 1) are all within the range ofalternative values selected by Blomback and Eckersten(1998) and Blomback et al. (1998). As it was intended touse the model in a predictive manner, a common set ofparameters was selected as far as possible for each crop

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Table 1

Adjusted parameter values in crop growth model for barley

Parameter Spring barley Winterbarley

1996,1997 1998

Photosynthesis radiationuse efficiency underoptimal temperature,water and N conditions,g[DM] MJ�1 (ofglobal radiation)

2�5 2�5 4�5

Maximum lifetime ofleaves, days

110 80 110

Maximum Nconcentration

of leaf biomass, fraction

0�035 0�035 0�045


of stem biomass,fraction

0�006 0�006 0�010


of root biomass, fraction

0�006 0�006 0�010

Fraction of mineral Navailable formineralisation andplant uptake,fraction day�1

0�07 0�07 0�07

Fraction of daily rootgrowth lost as litter

0 0�25* 0

Fraction of biomass lostas litter, day�1

Root 0�75 0�80 0�75Stem 0�013 0�025 0�013Leaf 0�0005 0�001 0�0005

*For first 8 weeks from sowing date only, zero thereafter.

0

100

200

300

400

500

600

700

26−Mar−96(a)

(b)

26−Mar−97 26−Mar−98

Cro

p co

mpo

nent

bio

mas

s, g

/m2

0

2

4

6

8

10

12

26−Mar−96 26−Mar−97 26−Mar−98

Cro

p co

mpo

nent

N c

onte

nt, g

[N]/

m2

Fig. 3. Simulated pattern of development of cereal cropcomponents: (a) biomass yield; (b) N content; , grain;

, stem; , leaf; , root


(spring barley and winter barley), with no variationfrom year to year. The one exception to this was madefor the spring barley crop in 1998, where very wet soilconditions at sowing time led to delayed and poorestablishment of the crop, and in turn to low grain andstraw yields. An alternative set of parameter values wasselected (Table 1), mainly to represent a higher rate ofroot death during the period with waterlogged soil.

4.3. Adjustments in simulations representing ley crop

The ley period prior to the current experiment was anexperiment on grazing sheep on swards established in

1987, as described by Swift et al. (1993) and Vipondet al. (1997). Plots had either grass swards receivingmineral N (170 kg[N] ha�1) annually, or grass/cloverswards receiving no mineral N. For another study,parameters had been selected for a simulation with thelinked grass growth and SOILN models to representgrazing dairy cows at another site (McGechan & Topp,2004). These were adapted for the fertilised grass swardsin the current study by selecting appropriate soil andfertiliser level values. Davies (1996) carried out extensivedetailed measurements of soil OM contents at the site,noting that immediately prior to ploughing there was alarge OM pool derived from dead plant material at oron top of the soil surface, which he described as ‘turf’. Inorder to allow this pool to build up to the appropriatelevel in simulations, the rate constant for transfer ofsurface litter to the litter pool in the uppermost soil layerwas reduced from the previously assumed higher valueof 0�05 to 0�003 day�1.

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4.4. Soil heat and water simulations

A simulation with the SOILN model must bepreceded by a weather-driven simulation with the soilheat and water model SOIL (Jansson, 1996). Thisdetermines the soil temperature and water content,since transformation rates in SOILN are temperatureand wetness dependent, as well as water movements,which transport dissolved nitrate to field drains orgroundwater. Calibration and testing of the SOIL modelfor various instrumented drained plot sites, includingone with a clay loam soil about 1 km distant from theBeechgrove field, has been described by McGechan et al.(1997). This entailed measurement of near saturatedhydraulic conductivities using a tension infiltrometertechnique (Ankeny et al., 1988), as well as measurementof water release characteristics of the soil. Similarmeasurements were made for the clay loam soil in theBeechgrove field for the current study (Table 2). TheBeechgrove site was not fitted with monitoring equip-ment to measure drainflows, so testing the SOIL modelwas confined to comparing simulated soil temperaturesand water contents with measured data. Simulatedsoil temperatures were a very good fit to measurements(Fig. 4). Simulation of soil water contents were reason-

Table 2Physical and hydraulic parameters of clay loam soil in Beech-

grove field

Horizon A A Bsurface main

Layer depth, m 0–0�1 0�1–0�3 0�3–1�0Porosity, % 53�0 50�7 47�9Dry bulk density, kg m�3 1�25 1�34 1�43Pore size distribution index 0�140 0�067 0�055Soil water tension at air

entry, kPa0�31 0�15 0�18

Residual water content, %volumetric basis

7�5 7�5 7�5

Macroporosity, % 4�0 4�0 4�0‘Break point’ tension, kPa 0�598 0�640 1�192Saturated hydraulic

conductivity (includingmacropore flow)mm day�1

5000 3000 60

Hydraulic conductivity atbreak point, mm day�1

102 45�2 10

Water content at 5 kPatension, % volumetricbasis

38�3 41�7 41�2

Maximum deep percolationflow, mm day�1

0�25

Distribution of rootdensity, fraction

0�33 0�55 0�12

Drain spacing, m 7�5Drain depth, m 0�5

able fits, bearing in mind the extent of variation invalues measured by different methods, or differentsensors for each method, at any one point in time(Fig. 5). However, the timing of the start of drying outduring dry soil periods in summer was less good. Thisimplies a shortcoming in the representation of evapo-transpiration, since further adjustments to parametervalues gave no improvement to this timing withoutcompromising fits to water content data.

5. Simulated results

5.1. CO2 production in laboratory incubations

Laboratory incubations described by Vinten et al.(2002) were carried out on soil samples taken from sixdistinct equal layers of the topsoil down to a depth of0�3 m immediately prior to ploughing (Fig. 6). Aspreadsheet model was set up to represent decomposi-tion of humus and litter pools according to first-orderdecay relationships from the initial OM pool sizes ineach soil layer prior to ploughing as measured by Vintenet al. (2002). The quantity of CO2 produced by thisdecomposition was also estimated, assuming for thelitter pool values of the efficiency factors fe and fh of 0�48and 0�52, respectively. These are the same efficiencyfactor values as those selected on the basis of literaturesources and experiments for a parameterisation ofSOILN for a clay loam soil in a field adjacent to thecurrent study site by Wu et al. (1998), and these valueswill be assumed throughout the current study.

In order to obtain simulated values of CO2 productionsimilar to those measured in the laboratory (Fig. 6), it wasnecessary to select decomposition rates which declinedprogressively following the start of the incubationexperiment. In practice this decline will be gradual, butfor convenience step changes were assumed as anapproximation, taking place 8 and 29 days after the start(Table 3). These changes in decomposition rates suggestthat, although the samples were supposedly intact soilcores, some disturbance to the protected component ofOM must have taken place, similar to that which occurs inthe field due to ploughing. The effects of different startingOM values was investigated by carrying out simulationswith starting OM values corresponding to the two grassplots on which these measurements had been carried outin detail. However, the pattern of simulated CO2

production was almost identical for the two plots.

5.2. CO2 production in field trials

Simulations with the SOILN model were carried outin an attempt to reproduce the CO2 production

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0

5

10

15

20

Depth 25 mm

0

5

10

15

20

Depth 75 mm

0

5

10

15

20

Depth 175 mm

Soil

tem

per

ature

, °C

0

5

10

15

20

1−Jan−97 2−Apr−97 2−Jul−97 1−Oct−97 31−Dec−97 1−Apr−98

Depth 300 mm

Fig. 4. Soil temperature at various depths simulated with SOIL model compared to measurements: , simulation with SOILmodel; +, measured by thermistor


measured in some of the field plots. Detailed dailymeasurements of CO2 production were made for aperiod following ploughing and establishment of springbarley in 1996 with a fertiliser rate of 80 kg ha�1 (Fig. 7).Cumulative total CO2 emissions were also measuredover a number of time periods during 1996, 1997 and1998 with varying levels of fertiliser application and forboth spring barley and winter barley crops. The plots onwhich measurements were carried out included zerocultivations and ploughing to two depths, but simula-tions were carried out to represent only those ploughedto a depth of 0�2 m. Initial OM pool sizes in each soillayer were again based on those measured by Vinten et

al. (2002), but in this case the OM in the so-called turfpool was assumed to be distributed evenly down to theplough depth after ploughing. The litter in each topsoil

layer was thus the sum of that measured in samplestaken before ploughing plus that from the turf pool. Thequantity of CO2 produced by decomposition was againestimated, assuming the same efficiency factor values(fe and fh) of 0�48 and 0�52 for the litter pool.

In order to obtain simulated values of CO2 produc-tion similar to daily field measurements (Fig. 7), it wasnecessary to select a decomposition rate for the humuspool which declined progressively following the date ofploughing. Step changes were again assumed as anapproximation to a smooth decline, taking place 7 and28 days after the start (Table 3). Simulations assuming aconstant low value of the decomposition rate gave CO2

production much lower than measured (Fig. 7). Thechanges in decomposition rates needed to improve fitssupport the supposition that disturbance by ploughing

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Soil

wat

er c

onte

nt b

y vo

lum

e, f

ract

ion

0.10

0.20

0.30

0.40

0.50

Depth 25 mm

0.10

0.20

0.30

0.40

0.50

Depth 175 mm

0.10

0.20

0.30

0.40

0.50

1−Jan−97 2−Apr−97 2−Jul−97 1−Oct−97 31−Dec−97 1−Apr−98

Depth 300 mm

0.10

0.20

0.30

0.40

0.50

Depth 75 mm

Fig. 5. Soil water content at various depths simulated with SOIL model compared to measurements made by various methods:, simulation with SOIL model; , , measured by time domain reflectrometry (TDR) at two locations; n, B,

‘ThetaMeter’ (commercial sensor) at two locations; �, core sample


affects the protected component of OM, temporarilyconverting it to unprotected OM which decomposes at ahigher rate. In contrast to the situation in the laboratoryexperiments, it was necessary to assume no initial raiseddecomposition rate for the litter pool, rather a slightincrease from an initial low value to the long-term valueestimated from the laboratory experiments. A possiblejustification for this is that the material in the litter poolin the field experiments was dominated by the dis-tributed turf, whereas in the laboratory experimentsthere was only a small quantity of dead plant derivedmaterial and this would have been from plants whichhad died a long time previously. There is evidence fromrecent unpublished experimental work (I. Bingham,pers. comm.) that the process of root death is not

instantaneous but takes place gradually over a period ofup to 40 days following destruction of a grassland plantby ploughing. This implies that there is likely to be aslow build-up in the rate at which plant-derived litterdecomposes, and such an effect would have been verysignificant for the large pool of turf material in the fieldexperiments.

A further adjustment was added to the decompositionrates for the years subsequent to that in which ploughingout of grass took place. Decomposition rates insubsequent years, during which only cumulative CO2

emission total values were collected, were assumed to beat half the level of those in the first year, with the sameproportionate change following ploughing (Table 3).This was justified by assuming that there would be a

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large build-up of protected OM during the longgrass-phase period with no disturbance by ploughing,which would become unprotected during the initialploughing out. In contrast, in subsequent years thebuild-up of protected OM would be for 1 yr only. Whilethe level and pattern of varying decomposition rate forthe protected/unprotected component of OM would besimilar in all years, the quantity of material in this poolwould be higher in the first year as a proportion of thetotal OM in the humus pool (see Section 3.4). Theoverall decomposition rate of the humus pool, includingthe accelerated decomposition due to ploughing, wouldtherefore be at lower levels in subsequent years. Thelower final decomposition rate in subsequent years is

0

20

40

60

80

100

120

140

160

0 50 100 150 200

Incubation time, days

CO

2 e

mit

ted

, g

/m2

Fig. 6. Simulated and measured cumulative CO2 emissions fromlaboratory incubation experiments carried out on samples oftopsoil from different depths at 12 1C: points–measured values;lines – simulations; B,- - - - - -, 0–5 cm; &, – – – –, 0–10 cm; m,– - – - –, 0–15 cm; � , , 0–20 cm; �, , 0–25 cm;

~, , 0–30 cm

Tabl

Decomposition rate coef

Material

(a) Laboratory incubation experimentsHumusLitter

Material

0–7

(b) Simulations representing field experiments*

Humus First year 0�00080Subsequent years 0�00040

Litter (with turf) First year 0�002Subsequent years 0�006

*Actual decomposition rates in simulations are adjusted to the simula

more consistent with those in literature sources and inthe previous calibration of the SOILN model (Wu et al.,1998) if account is taken of the 12 1C base temperatureTb compared to higher base temperatures in previousstudies.

Simulated CO2 emissions over the longer periodswhen cumulative emissions were recorded were reason-ably close to measured values for the spring barley plotsin 1996 and 1998, but for the spring barley plots in 1997and the winter barley plots in 1998 simulated valueswere considerably lower than measurements (Table 4). Itis not very clear why measured CO2 emissions were sohigh in the latter cases. However, the measurementtechnique used here was considered to be less accurate

e 3

ficients at 121C, day�1

Days after start of incubation

0–7 8–28 29–end

0�00024 0�000072 0�0000240�0145 0�0057 0�0029

Days after ploughing

8–28 29–59 60–119 120–next ploughing

0�00025 0�00008 0�00008 0�000080�00013 0�00004 0�00004 0�000040�002 0�002 0�003 0�0060�006 0�006 0�006 0�006

ted soil water content and temperature, as described in Section 3.2.

0

20

40

60

80

100

0 100 200 300 400 500 600 700Temperature sum at 5cm depth, degree−day

CO

2−C

em

itted

, g[C

]/m

2

Fig. 7. Simulated and measured cumulative CO2 emissions fromfield experiment carried out in spring barley plot with fertiliserrate 80 kg ha�1 during 1996, shown as a function of cumulativedegree-days at 5 cm soil depth: �, measured values; ,simulations assuming raised humus decomposition rate followingploughing; , simulations assuming constant low value of

humus decomposition rate

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Table 4

Measured and simulated CO2 and N2O emissions, cumulative totals over specified periods

Barleycrop

Fertiliser,kg[N] ha�1

Periodstart

Periodfinish

No ofdays

CO2, kg[C] ha�1 N2O N or denitrified N, kg[N] ha�1

Measured Simulated(1)

Simulated(2)

MeasuredN2O N

Simulateddenitrified N (1)

Simulateddenitrified N (2)

Spring 80 5/4/96 15/5/96 41 643 636 168Spring 80 24/5/96 12/6/96 20 1�24 0�00 0�00Spring 80 8/4/97 18/6/97 72 1245 604 436 3�52 5�50 3�51Spring 80 29/9/97 15/11/97 48 1927 309 260Spring 0 18/3/98 12/6/98 96 690 620 353 0�386 3�66 3�11Spring 80 18/3/98 12/6/98 96 678 574 322 2�61 6�18 5�17

Winter 0 18/3/98 12/6/98 96 2360 190 189 1�04 0�45 0�38Winter 120 18/3/98 12/6/98 96 4010 208 197 1�96 2�00 1�94

Simulation options: (1) raised decomposition rate after ploughing, (2) constant, low value of humus decomposition rate.


(compared to that used for the daily measurements in1996), and also CO2 from plant respiration was includedas well as CO2 emitted by the soil. Simulations assuminga constant low value of the decomposition rate gaveCO2 emissions much lower than measured values innearly every case. However, these cumulative measure-ments gave no indication of how decomposition rateschanged at the time of and after ploughing.

5.3. Denitrification

Cumulative total N2O emissions were measured overthe same time periods in 1996, 1997 and 1998 on thesame experimental plots (i.e. with the same treatments)as the cumulative CO2 emissions discussed in Section4.2. (Table 4). The SOILN model simulates denitrifica-tion, but gives no indication of the split of denitrified Nbetween N2 and N2O. The rate of denitrificationsimulated in SOILN depends mainly on soil wetness,and a rate constant appropriate to the particular soil.The rate constant assumed in earlier studies withSOILN gave very low rates of denitrification in thisinstance, whereas experimental data from the siteshowed quite significant N2O emissions. A rate constantof 1�5 g[N] m�2 day�1 was found to give a simulatedemissions over the measurement periods which wereabout double the measured values on two occasions forspring barley, suggesting that simulations are in agree-ment with measurements if denitrified N splits roughlyequally between N2 and N2O. However, in otherinstances simulated values were either much higher ormuch lower than double the measured values. For theperiod in May–June 1996 the simulated denitrificationwas zero because the simulated soil water content wasbelow the assumed threshold value for denitrification totake place, but this was obviously not the situation in

practice. The effect of assuming a constant low value forthe humus decomposition rate was a small reduction indenitrification compared to the situation with a raiseddecomposition rate following ploughing. It was con-cluded that the denitrification routines in the SOILNmodel do not give a very reliable indication of N lossesas N2O. While the SOILN model has been extensivelytested using nitrate leaching data (e.g. Wu et al., 1998),there has only been one known previous study testingSOILN with denitrification data, by Johnsson et al.(1991). However, the experimental data used byJohnsson et al. (1991) were based on an acetylene-inhibition method to prevent reduction of N2O to N2 soN2O produced corresponded to total denitrification, amethod not employed in the experiments of Vinten et al.(2002) on which the current study is based.

5.4. Crop yields and N uptakes

Although the experimental plots had four levels of Nfertiliser treatments (including zero), comparisons withsimulations were restricted to zero and the intermediatelevel (80 kg[N] ha�1 for spring barley or 120 kg[N] ha�1

for winter barley). Following some adjustments toparameters in the crop growth model as described inSection 4.2, simulated levels of crop and straw yield werefairly close to measured values (Fig. 8). The measuredyields showed a general fall-off over the years of theexperiment, which was fairly well reproduced in thesimulations with an increase in humus decompositionrate following ploughing. The effect of assuming aconstant low value for the humus decomposition ratewas a reduction in simulated yield compared to thesituation with a raised decomposition rate followingploughing, generally making the simulated yields lessclose to measured values, and losing any reproduction

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1

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96 97 98 97 98Spring barley Winter barley Spring barley Winter barley

Spring barley Winter barleySpring barley Winter barley

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, t[D

M]/

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96 97 98 97 98St

raw

yie

ld, t

[DM

]/ha

Fig. 8. Simulated and measured yields in field experiments during 1996, 1997 and 1998; grain yields (above) and straw yields(below); zero fertiliser (left) and with fertiliser application (right) at 80 kgN ha�1 for spring barley or 120 kgN ha�1 for winterbarley: ’, measured values; , simulations assuming raised humus decomposition rate following ploughing; &, simulations assuming

constant low value of humus decomposition rate

Table 5

Root mean square errors (RMSE) between measured and simulated yields and N uptakes

Fertiliser level, kg[N] ha�1 Humus decomposition rate Yield RMSE, t[DM] ha�1 N uptake RMSE, kg[N] ha�1

Grain Straw Grain Straw

Zero Variable 0�65 0�56 218 10�5Fixed 2�67 0�80 826 23�8

80 or 120 Variable 0�17 0�15 322 7�1Fixed 0�93 0�75 677 23�3

Overall RMSE are calculated from data for 3 yr of spring barley plus 2 yr of winter barley in each case. DM, dry matter.


of the fall-off in yield over the years. Root mean squareerrors (RMSE) between measured and simulated yieldslisted in Table 5 showed either a moderate or a verylarge reduction in RMSE with the varying comparedwith the constant humus decomposition rate. Regardinggrain and straw N offtakes, the situation mirrored thatshown by yields, with simulated values fairly close tomeasurements but slightly less consistent representationof year-to-year trends (Fig. 9). Again, assuming aconstant low value for the humus decomposition rategave a poorer representation of absolute values and ofyear-to-year trends, with higher RMSE (Table 5),compared to assuming a raised decomposition ratefollowing ploughing.

5.5. Soil inorganic nitrogen

Soil inorganic N (ammonium and nitrate) had beenmeasured in the experiments over a period during1997–98 for some treatment plots only. Simulatedvalues tended to be somewhat lower than the measuredvalues, but did show a rise during the period immedi-ately following application of mineral fertiliser (Fig. 10).

5.6. Soil organic matter

Final soil OM levels measured at the end of theexperiment (in Spring 1990), for the zero fertiliser spring

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N]/

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Fig. 9. Simulated and measured N offtakes in field experiments during 1996, 1997 and 1998; offtakes in grain (above) and in straw(below); zero fertiliser (left) and with fertiliser application (right) at 80 kgN ha�1 for spring barley or 120 kgN ha�1 for winterbarley: ’, measured values; , simulations assuming raised humus decomposition rate following ploughing; &, simulations assuming

constant low value of humus decomposition rate

0(a)

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35 725 35 825 35 925 36 025

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35 725 35 825 35 925 36 025

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ic N

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nce

ntr

atio

n,

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h−1

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rgan

ic N

co

nce

ntr

atio

n,

mg

h−1

Fig. 10. Simulated and measured soil inorganic N (NH4–N andNO3–N) in samples from field experiments: points – measuredvalues; lines – simulations; m, , nitrate N; , ————,

ammonium N; (a), spring barley; (b), winter barley


barley plots showed a large variability between plotsfrom almost no change (compared to before initialploughing) to a fall of around 10 t ha�1 in organic Cover 3 yr. Simulations for the zero fertiliser spring barleyplots showed a fall of about 5 t ha�1 (Table 6), areasonable compromise between widely varying experi-mental data, with a slightly higher fall in simulationswith a raised humus decomposition rate followingploughing compared to a constant low rate. The effectof varying the initial OM pools to correspond to each ofthe four main plots prior to ploughing (two with grass/clover and two with grass swards, although the initialOM values had not been made for the second grassplot), was investigated. This was found to have almostno effect, either on the change in organic C or on cropyields and N offtakes, even between the plots withdifferent ley crops.

5.7. Overall N balances

Simulated overall N balances between the start(assumed to be immediately before initial ploughingon 4 April 1996 for spring barley or 24 September 1996

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Table 6

Simulated C balances (topsoil, to 0.3m depth), from before initial ploughing (on 4 April 1996 for spring barley or 24 September 1996

for winter barley) to 28 February 1999, kg[C] ha�1

Raised humus decomposition rate after ploughing Constant low value of humus decomposition rate

Barley crop Spring Spring Winter Winter Spring Spring Winter Winter

Annual fertiliser,kg[N] ha�1

0 80 0 120 0 80 0 120

Initial C 112�8 112�8 112�8 112�8 112�8 112�8 112�8 112�8Final C 106�4 107�3 108�8 110�4 107�0 108�4 109�3 111�0Change in C �6�4 �5�5 �4�0 �2�4 �5�8 �4�4 �3�5 �1�8

Table 7

Simulated N balances, from before initial ploughing (on 4 April 1996 for spring barley or 24 September 1996 for winter barley) to28 February 1999, kg[N] ha�1

Raised humus decomposition rate after ploughing Constant low value of humus decomposition rate

Barley crop Spring Spring Winter Winter Spring Spring Winter Winter

Period of balance, days 1059 1059 886 886 1059 1059 886 886Annual fertiliser N 0 80 0 120 0 80 0 120Atmospheric deposition 74�1 74�1 66�0 66�0 71�7 74�1 66�0 66�0Fertiliser N (total) 0�0 240�0 0�0 240�0 0�0 240�0 0�0 240�0Change in litter N 245�9 232�2 153�8 121�9 273�9 247�0 168�3 133�7Change in humus N 139�7 101�3 42�5 14�7 3�8 �58�9 �64�8 �96�5Change in ammonium N �16�1 �0�6 �10�4 �0�5 �16�6 �0�5 �6�6 1�1Change in nitrate N �14�7 �21�8 �7�1 �7�8 �14�9 �19�8 �4�3 �4�0Harvested grain N 162�8 195�0 78�4 140�9 88�0 128�4 54�3 121�3Harvested straw N 18�7 21�6 7�9 12�9 11�6 14�0 6�5 11�5Denitrified N 131�1 143�1 53�6 61�6 122�0 122�4 37�0 44�8Leached nitrate N 116�3 265�5 100�3 214�1 94�0 217�2 56�0 158�0

Inputs 428�9 625�2 244�8 434�3 317�9 481�9 158�5 340�3Outputs 429�0 625�2 240�1 429�5 315�5 482�0 153�8 335�6

Daily leached nitrate N 0�110 0�251 0�113 0�242 0�089 0�205 0�063 0�178


for winter barley) and the end (assumed to be 28February 1999) of the experiment are presented inTable 7. In simulations with the raised humus decom-position rate after ploughing, there is a depletion of bothorganic N pools (litter and humus) which must make asubstantial contribution to the fertility of such low inputcereal systems, particularly if no fertiliser is applied.However, the contribution from the litter organic pool ismarkedly greater than from the humus organic pool.This arises mainly because of the large turf componentof the litter pool (see Section 5.2) which was assumed tobe present at the time of ploughing out of the ley crop.In comparison with the winter barley crop, thecontribution from the humus organic pool is larger forthe spring barley crop, where the raised decompositionrate after ploughing which mineralises N to an inorganicform (which can be used by the crop) is at a time ofrapid growth, when the crop can make good use of thisN. In contrast with the winter barley crop there is a long

time delay between the raised decomposition rate afterploughing mineralising some N and the time of rapidcrop growth, so the contribution from the humusorganic pool to fertility is lower. Where a constant lowvalue of the humus decomposition rate is assumed, thecontribution of organic N to fertility comes entirelyfrom the litter organic pool (including the turf compo-nent). In this case there is a build-up of humus organicN, which takes fertility out of the available N pool.

6. Predictive simulations

6.1. Options selected to simulate productivity of organic

cereal systems

Simulations were carried out in a predictive manner totest various options regarding low input or organiccereal production. In the first instance, the simulations

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set up to represent the zero fertiliser experimental plotswere extended to the end of 2001 to give three furtherharvests of spring or winter barley. To make compar-isons with conventional cereal production, a similarprocedure was adopted for the simulations representingplots receiving mineral fertiliser at 80 kg[N] ha�1 (forspring barley) or 120 kg[N] ha�1 (for winter barley). Forspring barley, the crop growth parameters (Table 1)were kept for all years at their standard values aspreviously selected for 1996 and 1997, rather than thoseselected for the atypical conditions of the 1998 experi-ment. Next, options of including organic N fertiliser asslurry or solid manure (FYM) were explored, for whichthe SOILN model had been calibrated and tested forvarious field sites in a previous study (Wu et al., 1998).To avoid problems of excess phosphorus (P) fertility,which in turn tends to cause high P leaching losses andconsequent environmental problems of eutrophicationof surface waters, application rates of slurry or FYM(Table 8) were selected to give an annual P applicationequal to the crop requirement for cereals of50 kg[P2O5] ha�1. These application rates and thequantities of N applied (Table 8) were estimated fromtypical slurry and FYM nutrient concentrations pre-sented by Dyson (1992). Quantities of ‘available’(ammoniacal) N were reduced further by a proceduredescribed previously by McGechan and Wu (1998)based on a combination of estimated losses in Dyson(1992) and the ammonia volatilisation model of Hutch-ings et al. (1996). In every case, the assumption wasmade about the temporary raised humus decompositionrate following ploughing, and that the humus decom-position rates in the first year would be higher than insubsequent years (Table 3b). Also, standard growth mo-del parameters were assumed for spring barley (Table 1)in all years, rather than changed values in 1998, since

Tabl

Details of slurry and manure (FYM)

Parameter

Total P2O5, kg�1t*

Total N, kg�1t*

Available (ammoniacal) N, kg�1t*

Spreading rate, t ha�1

Ammoniacal N (from composition and quantity), kg ha�1

Ammoniacal N entering soil after volatilisation losses, kg ha�1 (Ammoniacal N entering soil after volatilisation losses, kg ha�1 (N in faeces entering soil kg ha�1

N in bedding entering soil kg ha�1

*From Dyson (1992).

conditions at the test site in spring 1998 were consideredto be abnormal and atypical.

6.2. Results of simulations of productivity of low-input or

organic cereal systems

Results of simulations (Fig. 11) showed that, in theabsence of mineral fertiliser, there is a general decline inyields over the years following ploughing out of thepreceding grass ley, despite the presence of substantialquantities of N fertility transferred from the ley periodbut locked up in soil OM. Even allowing for access tosome of the N in protected OM following ploughing,yields fall within a short time to levels which might notbe considered economically viable. This compares withthe conventional system with mineral fertiliser whereyields can be held at a near constant level. The additionof organic fertiliser in the form of slurry or FYM allowsproductivity to remain at a high level similar to thatwhen mineral N fertiliser is applied.

6.3. Nitrate leaching

Unlike earlier studies in an adjacent field (Vintenet al., 1994; Vinten, 1999), the site of the current studywas not fitted with equipment to continuously monitornitrate leaching through the field drainage system. It waspossible only to make crude estimates of likely fieldnitrate leaching from analysis of water samples fromdipwells and of soil samples taken at irregular intervals.In such a situation, predictions from simulations mayprovide a more accurate estimate of leached nitratelosses.

Simulated leaching losses are shown as a componentof the N balances in Table 7 (also shown for the purpose

e 8

assumed for predictive simulations

Value

Slurry (7% dry matter) Solid manure (FYM)

1�61 3�53�15 6�51�54 1�6

31�0 14�347�8 22�9

spring barley) 29�6 14�2winter barley) 26�6 12�8

50�0 35�00 35�0

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Fig. 11. Grain yields in predictive simulations from 1996 to2001: (a), spring barley; (b), winter barley; , zerofertiliser; , mineral fertiliser applied at 80 kg ha�1 (springbarley) or 120 kg ha�1 (winter barley); , slurry applied;

- - � - -, manure (FYM) applied


of this comparison expressed on a daily basis for thediffering lengths of balance period) for the variousoptions tested. It was expected that leaching would be ata relatively high level for winter barley where there is along time gap between release of N from protected OMdue to ploughing in the autumn and the uptake of N bythe actively growing crop the following spring. Incontrast, lower levels of leaching were expected forspring barley since release of N from protected OM dueto ploughing in the spring occurs just before the periodof rapid uptake of N by the actively growing crop. Thisis the situation (when leaching is expressed per day) withsimulations with the raised decomposition rate afterploughing, but the difference in daily loss is not verylarge especially for the fertilised plots. However, itshould also be borne in mind that the winter barley plotswith higher daily losses had been ploughed only twice(with only two periods with the raised decompositionrate), compared to three times for the spring barleyplots.

7. Discussion

In this study of cereal production in a field which hadpreviously been grazed grassland, the linked SOILN andcereal growth models have been calibrated to represent aset of experimental data. In the first instance, parametervalues were selected on the basis of literature sourcesand previous calibrations of the linked models. Next,values of some parameters were adjusted to improve fitsto some of the data. The selected or adjusted valueswere, in several instances, a compromise to give as closeagreement as possible with experimentally measuredvalues for a range of variables, so there was someuncertainty about the choice of such parameter values.The decision to base the study on the SOILN model wasbased partly on it having an interactive crop growthmodel, and partly because of its simple representation ofsoil OM in two pools combined with the ability tochange decomposition rates during simulations. Avail-able data were considered adequate to calibrate thissimple structure but not a more complex structuremodel.

The modelling exercise described in this studyprovides a plausible explanation for some of theprocesses by which low input or organically growncrops receive their N fertility from components of soilOM. In particular, this work explores the concept of‘protected OM’, as component of soil OM whichbecomes ‘unprotected’ so N mineralises at a ratesomewhat faster than normal for a limited periodfollowing disturbance of the soil by ploughing. Thisraised mineralisation rate should raise the N supply andreduce N stress to the plant at a critical stage of growth.The results presented here appear to give some supportto this concept, with simulated yields being nearer to themeasured values with the raised mineralisation rate afterploughing compared to those assuming no change in themineralisation rate. The effect of disturbance ondecomposition of soil OM is an important aspect wherethere are gaps in knowledge and a need for moreexperimental data. There is also a strong case for a moredetailed experimental and mechanistic modelling studiesof the biological mechanisms underlying this process.While the decision was made to reject the option ofusing a model with a very complex system of soil OMpools, a case might be made for adding one new pool tothe two currently used in SOILN, at an intermediatestage (with an intermediate decomposition rate) betweenthe fast cycling litter pool and the slow cycling humuspool. This would be the pool containing the protected/unprotected OM for which the decomposition rate israised temporarily after ploughing. By putting thismaterial into a pool separate from humus OM, acommon set of varying decomposition rate parameters

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could be adopted both for years in which long-termgrass is ploughed out and for years where ploughing isof stubble from a previous cereal crop. A betterunderstanding is also required of the role of senescingplant material (both above ground and roots) in creatingnew soil OM. This may focus particularly on thecreation (and later destruction) of a large, partiallyliving pool of ‘turf’ material at the surface of the soilduring growth of ley crops. The work has also shown theimportance of taking account of delay followingploughing out of this material in the onset of decom-position, with a gradual build-up to the full rateobserved in the laboratory for other litter material suchas roots and other components of long-dead cerealplants. Denitrification and N2O emissions were not wellrepresented, but it was not known whether this was dueto inadequacies of the SOILN model or to variablequality experimental data.

Low input farming in general, and low input ororganic cereal production in particular, is very topical,and a number of other experimental trials on the subjectare currently in progress (e.g. Younie et al., 1995). It isanticipated that further modelling studies will be carriedout on datasets from such trials, in which there may beopportunities to make the required more detailedmeasurements. One result from this study which mayseem surprising is that N fixation in a grass/clover swardgave little benefit compared to a pure grass sward for thefertility of the cereal crop which followed ploughing outof the ley. In practice, faeces deposition, whether fromgrazing livestock or spread in manure or slurry, appearsto make a large contribution to the build-up of organicN in the soil during the ley phase of a rotation, whichmay dwarf the contribution from N fixation. Also in thiscase there was a relatively high C:N ratio by the time ofploughing, which may not be typical of plant materialderived from an N-fixing grass/clover crop. Anotherperhaps surprising result was that simulated nitrateleaching levels were only slightly higher with winterbarley compared to spring barley. It had been expectedthat these would be substantially higher in winter barley,where the release of N from temporarily unprotectedOM occurs at a time when crop uptakes of N are low,compared to the situation for spring barley where thetiming of release occurs just before the peak in cropdemand. Further studies to compare spring and wintercereal production in terms of fertility transfer andnitrate leaching would be justified. This study suggeststhat N fertility built up in soil OM during the ley phaseof a rotation can only maintain productivity of thearable phase for a limited period, and even then withlower yield levels, compared to a system where inorganicor organic N fertiliser is applied. However, viable yieldscan be maintained in an organic cereal system by

applying fertiliser in organic forms such as slurry orFYM.

8. Conclusions

The work described here should be regarded as apreliminary study, demonstrating the use of a calibratedsoil N dynamics model to explore various issues inrelation to low input and organic cereal production. Inparticular, it has illustrated the importance of the leyphase for providing fertility in such systems, and also ofmanagement decisions about the timing of cultivationsas well as those about the choice of winter or springsown crops. The role of modelling in for exploring suchissues has also been demonstrated, although there isscope for further model development supported byadditional experimental data to fill in gaps in knowledgethat have been identified. In this instance, there was noopportunity to test or ‘validate’ the models againstindependent datasets, a procedure which should ideallybe adopted in a modelling study. Further modeldevelopment and testing should lead to an improvedscientific understanding of important soil/plant interac-tion processes.

Acknowledgements

This work was funded by the Scottish ExecutiveEnvironment and Rural Affairs Department. Theauthors wish to acknowledge the support of their SACcolleagues who carried out the experimental work onwhich this modelling study was based, and of the SOIL/SOILN model development team at the SwedishUniversity of Agricultural Sciences for assistance withusing the models.

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