modelling environmental impacts of deposition of excreted nitrogen by grazing dairy cows

Agriculture, Ecosystems and Environment 103 (2004) 149–164

Modelling environmental impacts of deposition of excretednitrogen by grazing dairy cows

M.B. McGechan∗, C.F.E. ToppResearch Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK

Received 5 February 2003; received in revised form 23 September 2003; accepted 21 October 2003

Abstract

The soil nitrogen (N) and carbon dynamics model SOILN (which has interactive links to a grass growth model), and thedual-porosity contaminant transport model MACRO, have been used to study environmental pollution arising from grazingdairy cows. The models had been calibrated and tested in previous studies related to livestock agriculture. Information aboutN contents and other characteristics of urine and faeces excreted by dairy cows was assembled from literature sources.Watercourse pollution by nitrate and ammonium was the main environmental impact considered. Denitrified nitrogen losseswere also estimated as an indicator of nitrous oxide pollution of air. Higher levels of nitrate pollution in tile drains (whichfeed into watercourses) were shown to arise under grazing compared to fields receiving slurry and cut for silage. Much of thisraised nitrogenous pollution arises late in the grazing season. High levels of nitrate pollution could be attributed to variousfactors, including the fact that cows tend to congregate in certain areas of a field at a localised stocking rate much higher thanthe overall stocking rate, and due to deposition of N at times when grass cannot utilise it as a plant nutrient. The fact that urineand faeces patches are concentrated over a small proportion of the field area did not give an increase in overall loss when thiswas considered along with field areas receiving no excretions. Rapid transport through soil macropores of ammonium fromurine led to high pollution loads during grazing on wet soil. In contrast to leaching, simulated N losses by denitrification wereat a low level, and appeared to show little variation with factors which had a large effect on leaching losses. Overall, the formsof pollution most damaging to the environment due to spatially non-uniform excretion by grazing animals, appeared to beleached ammonium from urine transported by macropore flow, and leached nitrate exacerbated both due to cows congregatingand due to deposition at times of low plant N uptake.© 2003 Elsevier B.V. All rights reserved.

Keywords:Modelling; Nitrate; Pollution; Grazing; Excretion; Dairy cows

1. Introduction

Animal faeces and urine contain nutrients whichare valuable assets if they can be utilised by grow-ing plants, but they can cause serious environmentalpollution if they are leached to watercourses via tile

∗ Corresponding author. Tel.:+44-131-535-3029;fax: +44-131-535-3031.E-mail address:[email protected] (M.B. McGechan).

drains, or lost from the soil by denitrification. Simu-lation models representing soil water, solute transportand nutrient dynamics are useful tools for represen-tation of nutrient uptake and leaching processes. Outof a number of existing soil nitrogen (N) and car-bon dynamics models, the Swedish soil N dynamicsmodel SOILN is particularly suitable since it has in-teractive links with a crop growth and nutrient uptakemodel. Previous studies using such models to describeN applications to land have been concerned mainly

0167-8809/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/j.agee.2003.10.004

150 M.B. McGechan, C.F.E. Topp / Agriculture, Ecosystems and Environment 103 (2004) 149–164

with mineral fertiliser, manure (e.g.Blombäck et al.,1995) and slurry (e.g.Wu et al., 1998), which havebeen assumed to be evenly distributed over the sur-face. A simplistic approach to the grazing situationwould be to estimate N in excreta from the animals ata given stocking rate and assume this is evenly spreadover the field. However, this would ignore spatial vari-ability effects which lead to localised high concentra-tions in the vicinity of dung or urine patches, or inareas of fields where animals tend to congregate forshelter or around feed stalls or drinking troughs. Themain objective of this paper is to explore modellingapproaches to the study of spatial variability effectson environmental impacts of excretion from grazinganimals, where localised concentrations may lead tohigher levels of pollution than would arise with spa-tially uniform spreading. The effects of temporal dis-tribution, comparing the impacts of excretion over thewhole grazing season, compared with those from a sin-gle application of manure or slurry, are also explored.A further aspect concerns rapid transport of urinary Nthrough soil macropores, which is explored using thedual-porosity contaminant transport model MACRO,also from Sweden.

2. Modelling tools

2.1. SOILN

As in some previous studies concerning manure andslurry, the fate of N within the soil profile has been rep-resented using the SOILN model, which simulates allthe important transport and transformation processeswhich N undergoes in the soil. N is held in a numberof distinct pools, ammonium, nitrate, litter, faeces andhumus, with rate constants for dynamic transfer be-tween pools. The latter three of these pools describesoil organic matter (SOM) where carbon is also rep-resented, since decomposition of carbon (with someloss as CO2) controls the rate of mineralisation or im-mobilisation N flows. Litter and faeces are fast cy-cling SOM pools with relatively high decompositionrates, while humus is a slow cycling pool with a muchlower decomposition rate. Unlike most other soil Ndynamics models which represent crop uptake of Nby a fixed equation or curve, SOILN works interac-tively with a weather-driven crop growth model. Daily

uptake of N from the soil is controlled by the growthmodel, while constraints on crop growth due to short-age of N are controlled by the SOILN model. Forthe grassland situation, a grass growth model (Toppand Doyle, 1996) has been linked to SOILN (Wuand McGechan, 1998) in place of the standard cerealgrowth model (Eckersten and Jansson, 1991) providedwith SOILN. Simulations with the SOILN model mustbe run in conjunction with simulations using the soilwater and heat model SOIL, since transport of dis-solved nitrate depends on soil water movement, andall the transformation rates in SOILN are soil wet-ness and temperature dependent. Simulations with adaily output timestep with the linked SOIL, SOILNand crop growth models were carried out over 10 yearperiods, to account for weather variability effects.

2.2. MACRO

An alternative approach was taken to modellingrapid transport of excreted urine through the soil pro-file using the MACRO model (Jarvis, 1994). MACROis a ‘dual-porosity’ hydrological and solute transportmodel, with separate representation of water move-ments and solute concentrations in two ‘domains’, soilmatrix pores (micropores) and macropores, as well assolute movements between the two domains. Simula-tions with a one minute output timestep were carriedout over a 24 h period following urine deposition, butwith initial soil water contents selected for a particu-lar day from simulations with the SOIL model. Thisprocedure included an adaptation of the hydrologicalsimulations from MACRO, to represent the localisedhydrology arising due to high intensity water appli-cations in the vicinity of a urine patch, as previouslydescribed byMcGechan (2003a). A similar approachto modelling rapid transport of urine derived ammo-nium following slurry spreading has been describedby McGechan (2003b).

2.3. Calibration of models

The models were calibrated for an experimentaldrained plot site in a grassland field with a silty clayloam soil on a dairy farm at Dumfries, SW Scotland.The site was managed mainly for silage making withtwo or three cuts, but with some aftermath grazing.Animal slurry (liquid manure) was applied once each

M.B. McGechan, C.F.E. Topp / Agriculture, Ecosystems and Environment 103 (2004) 149–164 151

winter, with further spreadings after some silage cuts.Mineral N fertiliser was also applied. This site wasinstrumented to record water flows and solute concen-trations through the tile drainage system which haddrains at a spacing of 7 m and depth 0.65 m. Deep per-colation flows to groundwater were estimated as beingat a low rate compared to flows to surface waters viathe tile drainage system. Drainage and nitrate leachingdata has been presented byHooda et al. (1998)andVinten et al. (1991, 1994). Calibration of the SOILmodel for several sites, including measurement ofsoil hydraulic parameters, together with testing withdrainflow data, have been described byMcGechanet al. (1997). For the grassland site at Dumfries, thecalibrated SOIL model gave a good representation ofthe pattern of drainflows, but with a slight underpre-diction of the cumulative flow over an extended pe-riod. A similar calibration of the hydrological routinesin MACRO has been described byMcGechan (2002).Calibration of a MACRO representation of ammoniumleaching through drains following slurry spreadinghas been described byMcGechan (2003b). This cal-ibration was tested against data recorded at an east ofScotland site with instrumented drains (Parkes et al.,1997). Simulated results showed good agreement withmeasurements, which in some cases indicated leach-ing of up to half the urine derived ammonium over a48 h period after spreading. Calibration of the SOILNmodel linked to the grass growth model has been de-scribed byWu et al. (1998). This calibration was alsotested against nitrate leaching data collected at the in-strumented drained plot site at Dumfries (Hooda et al.,1998). As with drainflows simulated with SOIL, thecalibrated SOILN model gave a good representationof the pattern of nitrate leaching losses, but with aslight underprediction of the cumulative loss over anextended period. However, there was no instrumenta-tion at this site to test the predictions of denitrificationmade by the SOILN model. Some testing of denitrifi-cation predictions from SOILN has been reported inearlier studies, e.g.Johnsson et al. (1991).

3. Excretion by dairy cows

3.1. Quantities of N excreted during grazing

Quantities of N excreted by grazing dairy cows wereestimated as 234 g N per cow per day in urine and

121 g N per cow per day in faeces. These were calcu-lated from figures for 102 adult dairy cows presentedfor a model dairy farm byJarvis (1993), assuminga grazing period of 182 days. Similar daily valueswere estimated from data presented byHutchings et al.(1996) and for figures for 165 ‘livestock units’ (in-cluding some calves) presented byJarvis (1993). Theestimated daily quantity of urine N was then reducedto 214 g N per cow per day entering the soil, assum-ing 20 g N per cow per day (8.6%) would be lost byvolatilisation (based onHutchings et al., 1996), whileall excreted faeces N was assumed to enter the soil.Another set of estimates fromDyson (1992)was inagreement for faeces N, but (because it representedstored slurry with much higher volatilisation losses)the figure for urine N was much lower.

3.2. Dung and urine patch details

Assumptions about dung and urine patches werebased on values presented byLantinga et al. (1987),although similar figures (or ranges of values in somecases) were also presented byMacLusky (1960),Richards and Wolton (1976)andHaynes and Williams(1993). Adult dairy cows were assumed to produce12 urinations of 3.5 l, plus 12 defaecations of 1.0 l,each day in the field. A dung patch covers 0.05 m2.Lantinga et al. (1987)suggest an initial urine patcharea of around 0.3 m2, but that it will have an influ-ence (in terms of affecting crop growth) over 0.68 m2.However, in this study with the soil hydraulic param-eters for the Dumfries grassland site, work with theMACRO model indicated that the urine patch wouldspread over 0.73 m2 (with 4.5 mm of liquid infiltratingover this area). The estimated localised concentra-tions of N entering the soil (based on estimated dailyquantities of N excreted) were 2011 kg N ha−1 in adung patch covering 0.05 m2 (with a concentration of10.1 g N l−1) and 244 kg N ha−1 in a urine patch cov-ering 0.73 m2 (with a concentration of 5.1 g N l−1).

Petersen et al. (1956), Richards and Wolton (1976)andHack-ten Broeke et al. (1996)have discussed theprobability of dung or urine patches being depositedover areas contaminated by recent previous defaeca-tions or urinations.Petersen et al. (1956)fitted a bino-mial distribution to experimental data describing spa-tial distribution of excreta from grazing animals. Fora typical stocking densities of 4 cows ha−1, 12 daily


defaecations cover 0.024% of the land area, while 12daily urinations cover 0.35% of the land area, so dou-ble contamination over a time interval of a few days isa relatively rare occurrence. An approach to multiplecontamination within a single grazing season adoptedin the current study is described later in this paper.

3.3. Overall and localised stocking densities

Jarvis (1993)assumed an overall stocking density(in ‘livestock units’ ha−1) of 2.2 for a typical dairyfarm. However, since up to half the grassland area istypically shut off for conservation as hay or silage,an average stocking rate of 4 dairy cows ha−1 over thegrazed area only was assumed in most cases for thisstudy.

There is also evidence from general observationsand literature sources regarding localised higher stock-ing rates in certain parts of a grazed field.Shiyomiand Tsuiki (1999)describe a model of ‘troop length’,the distance between the most distantly spaced pair ofanimals, in a grazing herd. Observations in the fieldshowed that moving animals had a mean troop lengthsimilar to the theoretically derived value for randompositioning, while this was lower by a factor of about2 for actively grazing animals and about 4 for restinganimals.Petersen et al. (1956)observed, by countingexcretions per unit area after fields had been grazed,that cows spend more time in the vicinity of watersources and along fences than in the middle of a field.Similarly, Franzluebbers et al. (2000)observed higherinorganic N concentrations arising from excreta in ar-eas adjacent to shade or water sources.White et al.(2001) observed densities of urinations by lactatingdairy cows up to 10 times as high near to a watertank compared to the furthest away part of the field;this effect was most marked following hot weather. Innorthern European climates, inclement weather leadsto cows congregating in areas of fields which providemost shelter from wind or rain.

4. Approaches to modelling spatial variability

4.1. SOILN with no inputs from grazing animals

A simulation was carried out with SOILN to rep-resent a non-grazed grassland field receiving only

mineral fertiliser, with herbage removed in three cuts(for silage making). This was to provide a base levelof N losses from which raised levels arising due to ex-cretion by grazed animals could be calculated by dif-ference. In this and all SOILN simulations describedsubsequently (inSections 4.2–4.7 and 4.9–4.12),an annual mineral fertiliser input of 260 kg N ha−1

was assumed, based on the typical dairy farm de-scribed byJarvis (1993). Simulations were carriedout over 10 years of weather data to determine theaverage annual N losses in nitrate leaching and bydenitrification.

4.2. SOILN with uniform distribution at averagestocking rate

The next simulation carried out with SOILN (againcarried out over 10 years of weather data) representeda field grazed by dairy cows at a uniform stockingdensity of 4 cows ha−1 over a summer grazing seasonlasting 182 days. This provided a further base levelfrom which to judge raised levels of nitrate leachingand denitrification due to high or non-uniform stock-ing rates. To represent discrete excretions in this simu-lation, the total quantity of faeces and urine producedover the grazing period (based on quantities per cowper day as discussed inSection 3.1) was divided intosix identical applications occurring on 15 April–15September inclusive.

4.3. SOILN with uniform distribution at raisedinput levels

Another set of simulations with SOILN assumeda range of stocking densities up to 16 cows ha−1,much higher than any typical overall stocking densityto represent a localised higher stocking density asfound in areas of the field where cows tend to con-gregate. Stocking densities of 2, 4 and 8 cows ha−1

were also considered representing alternative overallstocking rates which might be considered as optionsregarding management policies, as described byToppand McGechan (2003). Again the total quantity ofurine and faeces was divided into six identical andevenly spaced applications, with simulations carriedout over 10 years of weather data to determine av-erage annual N losses as nitrate leaching and bydenitrification.


4.4. Overall losses taking account of variablestocking rate within a field

For a given overall stocking rate, areas where cowscongregate at a higher localised stocking rate must bematched by larger areas with a below average stock-ing rate. To represent this situation, one fairly extreme(but not improbable) situation was investigated. Thisassumed that for half of each day all cows would con-gregate (for shade, shelter, to feed, drink water, etc.)at eight times the overall stocking rate over one-eighthof the field area, while for the remaining half of theday they would be evenly distributed (while grazingor searching for grass). Taken over the whole day, ex-cretions would be for 4.5 times the average stockingrate over one eighth of the field and half the averagestocking rate for the remaining seven eighths of thearea. From the results of 10-year simulations carriedout for stocking rates of half and four times the stan-dard rate, leaching and denitrification losses over halfthe day were determined by summing seven-eighthsof one plus one-eighth of the other. The mean lossesover the whole day were then determined as the aver-age of those on the half day with cows congregatingand the half day with the overall average stocking rate.

4.5. SOILN simulations representing slurryapplications

As a basis for a comparison of temporal variabilityeffects of excreta applications, a set of simulations wascarried out representing slurry applications to grass-land used for silage cutting. This assumed three silagecuts, on 20 May, 7 July and 30 August each year. Fivealternative dates for slurry spreading were considered,15 October, 31 December, 1 February, 15 March (asin a previous study byMcGechan and Wu, 1998) andafter the first silage cut on 21 May. An overall stock-ing rate for the farm of 2 cows ha−1 was assumed, withthe grass area divided equally between grazing fieldsand fields shut off for silage cutting and slurry spread-ing, so the stocking rate over the grazing fields wouldbe 4 cows ha−1. The volume of slurry (ha−1) over a26-week housing period was estimated from the quan-tities of faeces and urine produced per cow per daydiscussed inSection 3.1, assuming that the overall drymatter content of the mixed faeces and urine is 10%.However, a more typical dry matter content of slurry

when land spread is 7% due to the addition of rain-water and dilute dairy washings. Adjustment for thisdilution gives a slurry quantity equivalent to an appli-cation rate of 50 t ha−1, which was assumed in simu-lations. Assumed concentrations were 1540 mg N l−1

of urinary (ammoniacal) N and 1610 mg N l−1 of fae-ces (organic) N based on typical figures fromDyson(1992)for slurry leaving a storage area. Allowing forthe dilution factor, the quantity of faeces N is veryclose to that estimated from the quantity excreted percow per day, whereas the quantity of ammoniacal Nis much lower than that excreted due to volatilisa-tion losses in the animal house and during storage.The concentration of urinary N was then further re-duced to represent losses by ammonia volatilisationduring field application, by a procedure previously de-scribed byMcGechan and Wu (1998)involving run-ning the weather-driven ammonia volatilisation modelfrom Hutchings et al. (1996).

4.6. Parameters of grass growth model

For all the grazing simulations with the SOILNmodel described so far, the associated grass growthsub-model was adjusted to represent a grazed crop.This was set up with representation of offtake insix ‘cuts’ on 15 April–15 September inclusive, cor-responding to the timing of excreta applications asdescribed inSection 4.2. However, the proportionof leaf and stem material removed in each offtakeevent was reduced from those assumed for a silagecut (Table 1). The proportions were adjusted for eachstocking rate to give grazing offtakes similar to thosemeasured byBartholemew and Chestnutt (1977)andMorrison et al. (1980)in experiments in which grasswas cut at frequent intervals (Table 1). With stockingrates in excess of 4 cows ha−1, cows must receivesupplementary feed, so offtakes of leaf and stem wereset as for 4 cows ha−1. In the case of high localisedstocking rates due to cows congregating in certainareas of the field, this supplementary feed is grassgrazed from areas other than where the high level ofexcretion takes place.

4.7. SOILN simulations of conditions below a singledung patch

Simulations were carried out using SOILN witha single application of faeces at the very high input


Table 1Fractions of plant components removed by grazing and cutting

Grazing number (grazing date)

1 (15 April) 2 (15 May) 3 (15 June) 4 (15 July) 5 (15 August) 6 (15 September)

Stocking rate of 4 cows ha−1 or overFraction of leaf eaten 0.60 0.70 0.65 0.60 0.55 0.45Fraction of stem eaten 0.50 0.60 0.55 0.50 0.45 0.35

Stocking rate of 2 cows ha−1

Fraction of leaf eaten 0.25 0.30 0.27 0.27 0.22 0.18Fraction of stem eaten 0.20 0.25 0.22 0.20 0.18 0.13

Cut number (cut date)

1 (19 May) 2 (05 July) 3 (27 August)

Cutting for silageFraction of leaf removed

by cutting0.98 0.98 0.98

Fraction of stemremoved by cutting

0.90 0.90 0.90

level (2011 kg N ha−1 as derived from the quantityproduced per cow per day and the dimensions ofthe patch) occurring locally under the dung patch.Simulations were continued to the end of the sec-ond winter following the application as it was foundthat raised levels of nitrate leaching and denitrifi-cation extended over this period. The effect of asingle faeces application each day over a period 1April–31 December during each of ten years wastested.

4.8. MACRO simulations of conditions below asingle urine patch

Simulations with MACRO were carried out withan application (described in MACRO as an irrigation)of 4.5 mm of water with a solute concentration (am-monium N) of 5.10 g l−1. A 1 min output timestepwas selected for these simulations which were set torun for 10 days, although most leaching took placeonly during the first 24 h period following urine de-position. The effect of a single urine application eachday over the period 1 April–31 December duringeach of 10 years was tested. Initial soil water contentswere selected for a particular day from simulationswith the SOIL model. Leaching of ammonium Nwas expressed as a proportion of that applied in theexcretion.

4.9. SOILN simulations of conditions below a singleurine patch

Simulations of conditions below a single urine patchwere carried out by a procedure similar to that adoptedto represent conditions below a single dung patch, withone urine application of 244 kg N ha−1.

4.10. Occurrence of coincident dung and urinepatches

A simple computer program was written to investi-gate the frequency of occurrence of dung and/or urinepatches overlapping on the same area of a field dur-ing one grazing season lasting 182 days. This as-sumed the frequencies of urinations and defaecationsper cow, as well as the urine and faeces patch di-mensions, as described inSections 3.1 and 3.2. Onehectare of field was divided into 200,000 imaginarysub-plots of 0.05 m2 (the area of the dung patch).Each of the 12 defaecations per cow per day was al-located by a random number generator to one of thesub-plots. Similarly, each of 12 urinations per cow perday was allocated to 15 different sub-plots (since oneurine patch covers an area 15 times that of a dungpatch). By this means, tabular distributions of numbersof sub-plots with all combinations of 0–10 urinationsand 0–4 defaecations were obtained for stocking rates


of 2, 4, 8, 12 and 16 cows ha−1. This showed largenumbers of sub-plots with only one urination or de-faecation, declining to very small numbers (or zero) ofsub-plots with the largest numbers of urinations anddefaecations.

4.11. SOILN to represent conditions undercoincident dung and urine patches

In order to investigate the local conditions undercoincident excretions, a set of simulations was car-ried out with SOILN to represent the sub-plots con-taminated by each of the combinations of coincidentdung and/or urine patches as derived inSection 4.10.Application dates for multiple urine or dung appli-cations were assumed to be evenly spaced through-out the grazing season (or at the mid-point date fora single urine or dung application). Simulations com-menced on 1 January in the year prior to the season inwhich excreta was applied, and continued to the end(31 March) of the second winter following, with min-eral fertiliser applied in each full year of the simula-tion. For each dung and urine option, the procedurewas repeated over 10 years of weather data so 10-yearaverage losses could be calculated.

4.12. SOILN to represent random placement ofdung and urine patches

For simulations with the SOILN model, the pro-cedure described inSection 4.11for allocating dungand urine patches to imaginary 0.05 m2 sub-plotswas adapted to randomly allocate each excretion toa particular week over a 26-week grazing season. Tosimplify the simulation procedure, if more than oneexcretion arose in a particular week on one sub-plot,the second (or third, etc.) was reallocated to the fol-lowing or preceding week so only one occurred ineach week. Simulations were carried out for each ofthe sample of 1000 such sub-plots for each of 10years. Since nitrate leached from each sub-plot wouldhave become mixed up in the drainage system, theoverall leaching loss was assumed to be the aver-age of that for each of 1000 sub-plots. Many of thesub-plots would have no excretions allocated at all, soleaching would be at the base level for land receivingmineral fertiliser only. The procedure was repeatedfor stocking densities of 2, 4, 8, 12 and 16 cows ha−1.

5. Results and discussion

5.1. SOILN assuming a localised uniformdistribution of excreta

Results from SOILN simulations representinggrazed grassland fields with a range of stocking ratesand a localised uniform distribution of excreta (i.e.ignoring concentration in patches), are presentedas plotted nitrate leaching and denitrification lossesagainst stocking rate inFig. 1, and as tabulated in-put/output N balances inTable 2. Results show littlevariation in nitrate leaching with stocking rates vary-ing in the range 0–4 cows ha−1. However, leachinglosses show a marked rise for stocking rates greaterthan 4, rising by a factor of 5 with a fourfold stockingrate rise from 4 to 16.

5.2. SOILN assuming cows congregatingin certain field areas

The effect on nitrate leaching of cows congregatingin certain field areas for part of the day was calcu-lated for the scenario described earlier and an overallstocking rate of 4 cows ha−1. This calculation wasbased on the leaching losses inTable 2for stockingrates of 16 (for half of the time over one eighth of thefield), 2 (for half of the time over seven eighths ofthe field) and 4 (for half of the time over the wholefield area). This gave an overall mean leaching loss of227 kg N ha−1, somewhat higher than 145 kg N ha−1

for a uniform stocking rate of 4 cows ha−1. The Nbalances (Table 2) show a significant proportion of Nis transformed to the humus pool with a stocking rateof 16 cows ha−1, but this is assuming an initial hu-mus pool size appropriate to the overall stocking rate.However, in the long term if the field is in permanentgrass without ploughing or reseeding, the humus poolsize will rise to a level appropriate to the localisedstocking rate. An alternative simulation with a higherinitial humus pool which remains approximatelyconstant at a stocking rate of 16 cows ha−1 gave alocalised annual leaching loss of 827 kg N ha−1, com-pared to 731 kg N ha−1 with the standard initial humuspool. This would further increase the overall levelof leaching loss (to 239 kg N ha−1) from a field withlocalised high stocking rates in small areas balancedby reduced stocking rates over the remainder of the


Fig. 1. Simulated nitrogen losses under grazing at a range of field stocking rates, mean annual values from 10-year simulations: (–�–)leached N; (– -�- –) denitrified N.

field. However, overall leaching loss level estimatesassume complete mixing of drainage water from dif-ferent parts of a field before it enters a watercourse. Ifthe area in which cows congregate is adjacent to sucha watercourse, significant leaching may take place atthe high concentration appropriate to the congregat-ing area. This supports the discussion byLine et al.(2000), of the importance of excluding livestock byfencing off areas near to watercourses to prevent highpollutant loads from such congregating areas.

In contrast to the effects on nitrate leaching, varia-tions in stocking rate appear to have almost no effecton levels of denitrification, which accounts for lessthan half as much loss of N at stocking rates up to4 cows ha−1 compared to nitrate leaching (Table 2).

Table 2Simulated input/output nitrogen balances under grazing at a range of field stocking rates, mean annual values from 10-year simulations

Grazing stocking density (cows ha−1)

0 2 4 8 12 16

N inputs (kg N ha−1)Faeces 0.0 43.9 87.8 175.6 263.4 351.2Urine 0.0 78.0 156.0 312.0 468.0 624.0Mineral fertiliser 260.0 260.0 260.0 260.0 260.0 260.0Atmospheric deposition 22.0 22.0 22.0 22.0 22.0 22.0

N outputs (kg N ha−1)Leaching 150.8 155.4 145.2 304.5 493.4 731.4Denitrification 65.0 63.3 55.9 51.1 44.2 44.1Harvested 0.0 166.0 371.7 396.9 396.4 391.5Humus change 57.5 21.7 −35.9 25.7 80.9 136.3

However, since denitrification predictions were nottested experimentally, it is not known whether theSOILN model adequately takes account of some fac-tors which might stimulate denitrification, such asincreased carbon supply from urine and faeces dis-tribution at high stocking densities leading to moreanaerobic conditions due to higher soil respiration.

5.3. MACRO simulations of ammonium losses fromurine excretion

Results from MACRO simulations of ammoniumleaching following urine applications show a verymarked relationship between loss level and soil wet-ness at the time of application (Fig. 2). For this


Fig. 2. Simulated cumulative ammonium losses 48 h after urinedeposition against soil wetness, indicated as air filled pore space,at the time of urination.

purpose, soil wetness has been described as air filledpore space ‘AFPS’ in the top 0.3 m of the soil profile;this is a more convenient measure than percentagesoil water content for which a different value wouldbe required in each soil layer, since each layer has adifferent porosity and other hydraulic parameter val-ues. These results are very similar to those found insimulations with MACRO of ammonium leaching fol-lowing slurry spreading (McGechan, 2003b). Whenurination takes place on wet soil, high leaching losses(sometimes more than half of the ammonium applied)occur within a few hours. Soil wetness is relatedmainly to the history of rainfall and evaporation ondays immediately prior to urination. However, rainfallon days following urination has almost no effect onsuch short duration high intensity leaching losses. Themacropore transport mechanism taking place here,and represented in the MACRO model, has been de-scribed in more detail byMcGechan (2002, 2003b).In contrast, when urination takes place on dry soilwhere macropores contain no water, losses are verylow or zero. Rapid transport of a soluble substancewhen applied to a wet soil, compared to very low mo-bility when applied to a dry soil, has been measuredand modelled byTillman et al. (1991). They alsoshowed that the quantity of rainfall or irrigation waterfalling some time after application of the substancehad a very small influence on losses compared to thesoil wetness at the time of application.

Results from the current study show that there isa clear level of soil wetness (or AFPS) above whichhigh losses occur. For the particular soil considered,the cut-off AFPS value is around 12 mm, but this willvary for different soil types. Tabulated values (10-yearaverages for each month considered) of numbers ofdays when this soil wetness level is exceeded, anddays when losses reach 1, 5 and 25% of urine applied,are listed inTable 3. As would be expected, there aremore days when the soil wetness limit is exceededin winter than summer months, but nevertheless thereare still a significant number of low wetness days inthe winter months. The choice of threshold loss levelwhich if exceeded represents a ‘high loss day’ hasa relatively small effect on the number of such highloss days each month. This arises because of the verystrong relationship between ammonium loss and soilwetness (Fig. 2), with a very steeply sloping curvein the region of the critical AFPS value. Mean lossesassuming grazing takes place every day, and alterna-tively for restricting grazing to days when soil is drierthan the 12 mm AFPS limit, are also listed inTable 3.Where grazing is allowed every day, mean losses arehigher for winter than for summer months. There is avery dramatic decrease in mean loss (to an almost in-significant level) where grazing is avoided on high soilwetness days, compared to grazing any day. These re-sults show a major environmental benefit from select-ing grazing days with drier soil conditions and keepinganimals housed on other days, particularly where thegrazing season is extended into October, November orDecember.

5.4. SOILN simulations of nitrate leaching dueto a single dung or urine patch

From simulations with SOILN representing a singleurine or dung patch deposited on a particular day, thequantity of nitrate leached (from the area of the patchonly) over the period from the date of deposition tothe end of the second winter (31 March) following de-position was determined. From this was subtracted thequantity of nitrate leached over the same period fromland receiving no excreta (mineral fertiliser only), toindicate the additional leaching which could be at-tributed to deposition of excreta. Plots of the effect ofdeposition on different days (shown for 10 individualyears) show a wide variation in the extent of the

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Table 3Numbers of high soil wetness days (indicated as low air filled pore space, ‘AFPS’), days with high ammonium losses, and mean ammonium losses. Monthly, six monthlyand annual mean values from 10-year simulations

January February March April May June July August September October November December October–March

April–September

Year

Number of low AFPS days 14.9 8.1 9.4 5.2 6.2 1.1 2.2 4.5 3.5 7.8 9.6 13.3 67.1 21.7 88.8Days with losses >1% 12.5 7.8 8.7 3.5 3.5 0.8 2.3 4.3 4.3 7.4 9.2 12.8 58.4 17.4 75.8Days with losses >5% 9.2 5.9 6.4 2.4 2.4 0.5 1.8 2.6 2.8 6.1 7.1 9.6 44.3 11.5 55.8Days with losses >25% 9.0 5.7 6.1 2.3 2.3 0.5 1.8 2.5 2.7 5.4 6.9 8.7 41.8 11.2 53.0Overall mean loss (%) 6.2 4.9 4.0 2.0 2.0 0.5 1.0 1.7 1.3 3.7 3.8 6.4 4.8 1.2 3.0Mean loss on low AFPS

days (%)12.1 15.9 12.7 11.5 8.4 9.3 9.8 11.6 9.9 13.6 11.2 15.6 12.6 9.6 12.0

Mean loss on high AFPSdays (%)

0.10 0.10 0.25 0.05 0.05 0.04 0.03 0.12 0.07 0.13 0.09 0.09 0.12 0.05 0.08


Fig. 3. Nitrate leaching loss from single urine or faeces patch against time of year at which deposition takes place. Shown as additionalloss (in kg N ha−1 from the area of the patch) up to the end of the second winter (31 March) following deposition compared to that fromland receiving no excretion. Shown for deposition in 10 individual years: (a) urine patch; (b) faeces patch.

additional leaching from the area of the urine ordung patch according to the timing of the application(Fig. 3). Deposition of urine over the period up tothe end of July when the grass crop is most activelygrowing and taking up N, leads to very little addi-tional N leached. The level of additional leaching forurine deposition from August onwards rises dramat-ically, reaching a peak (around 120–150 kg N ha−1)in mid-October, but tailing off for deposition towardsthe end of the year. Year-to-year variations are smallrelative to the effect of timing of deposition withinthe year. In contrast, for faeces deposition additionalleaching losses are at considerably higher levels(compared to those for urine deposition) of around200–350 kg N ha−1. Loss levels vary little accordingto the time of deposition, slightly higher for deposi-tion early in contrast to late in the grazing season,and also showing slightly more year-to-year variabil-ity than with urine deposition. For urine deposition,nearly all the additional leaching occurs during thefirst winter following urine deposition, with very lit-tle during the second winter (Fig. 4). However, for

faeces deposition most additional loss occurs dur-ing the first winter for deposition up to the end ofSeptember, but for deposition later in the season mostof the additional leaching occurs during the secondwinter period. Levels of leaching (per unit area) fromthe area covered by the patch are generally higher forfaeces than for urine, but since a urine patch coversan area 15 times as large as a dung patch, the overallquantity of additional nitrate leached is much greaterfor a urine patch. In every case where high lossesoccur, these can be attributed to deposition of N afterthe period of rapid grass growth so the crop is unableto extract much inorganic N so this remains in thesoil to be leached over the winter period.

5.5. SOILN simulations of denitrification lossesdue to a single dung or urine patch

From simulations with SOILN representing a singleurine or dung patch deposited on a particular day, thequantity of N lost by denitrification was determined.An unspecified proportion of this loss (possibly around


Fig. 4. Nitrate leaching loss from single urine or faeces patch against time of year at which deposition takes place. Shown as additionalloss (in kg N ha−1 from the area of the patch) up to the end of the first winter (—), from the end of the first winter up to the end of thesecond winter (- - -), and the total up to the end of the second winter (—) (31 March in each case) following deposition compared to thatfrom land receiving no excretion. Shown as mean values over 10 years: (a) urine patch; (b) faeces patch.

50%) will be lost as nitrous oxide, a highly pollut-ing greenhouse gas, the remainder being lost as N2which does not pollute air but represents an economicloss. As for nitrate leaching, this loss was estimatedover the period from the date of deposition to the endof the second winter (31 March) following deposi-tion, and from this was subtracted the denitrificationloss over the same period from land receiving no exc-reta (mineral fertiliser only), to indicate the additionalloss which could be attributed to deposition of exc-reta. Plots of the effect of deposition on different days(shown for 10 individual years) show much lower lev-els of loss by denitrification than those which occur byleaching, with little variation according to the timingof the application, and little variation between years(Fig. 5). Simulated levels of additional denitrificationloss (per unit area) from the area covered by the patchare higher for faeces than for urine, but again since aurine patch covers an area 15 times as large as a dungpatch, the overall quantity of additional denitrificationloss is likely to be much greater for a urine patch.

5.6. Comparison of losses under grazing andslurry spreading

Losses following slurry spreading at a rate of50 t ha−1 are shown inFig. 6. Leaching losses are ata low level for slurry spread in March or after thefirst silage cut (May), but at much higher levels withautumn or winter spreading. Low leaching lossesarise where slurry is spread at a time when the grassis actively growing so it can take up and utilise theslurry N, while high losses arise where this is notthe case. Leaching losses under grazing with a stock-ing density of 4 cows ha−1 uniformly distributed areconsiderably higher than those with slurry spreadingat all times considered, even when slurry is spreadin autumn or winter, which are not optimum slurryspreading times. This reflects the fact that the graz-ing period includes a portion when grass is activelygrowing, but also a portion after the end of the vigor-ous growth period, as illustrated by the plot of lossesfrom a single urine patch at different times (Figs. 4


Fig. 5. Denitrification N loss from single urine or faeces patch against time of year at which deposition takes place. Shown as additionalloss (in kg N ha−1 from the area of the patch) up to the end of the second winter (31 March) following deposition compared to that fromland receiving no excretion. Shown for deposition in 10 individual years: (a) urine patch; (b) faeces patch.

and 5). In contrast, slurry is generally spread just priorto the period of active grass growth, so much of theavailable N can be utilised by the crop. Also, sincea significant proportion of ammonium N in urine is

Fig. 6. Simulated nitrogen losses under slurry spreading at various dates, for grazing at 4 cows ha−1 field stocking rate (2 cows ha−1 overallfarm stocking rate), mean annual values from 10-year simulations: (–�–) leached N; (– -�- –) denitrified N.

lost by volatilisation in the animal house and in slurrystorage before entering the soil, there is less N re-maining to be converted to nitrate and subsequentlyleached.


Simulated N losses by denitrification remain atroughly the same level (for the particular soil con-sidered here) under both grazing and cutting/slurryspreading, with little variation for the different timingoptions in both cases. However, since losses of N byleaching are lower for slurry spreading than for graz-ing, denitrification losses following slurry spreadinggenerally exceed leaching losses, except where slurryis spread in October (Fig. 6).

5.7. SOILN simulations representing losses fromareas of coincident dung and urine patches

SOILN simulations were carried out representinga range of options for areas of land receiving morethan one defaecation and/or urination during a singlegrazing season. All combinations of 0–4 defaecationsplus 0–10 urinations (evenly spread over the season)were considered. The main effect was a high level ofleaching loss during the first winter period, from thearea receiving the multiple excretions. These lossesrose to an extremely high level of 3300 kg N ha−1 forthe combination of four defaecations plus 10 urina-tions. For areas receiving at least two urinations or atleast one defaecation, there was an approximately lin-ear relationship between loss level and the number ofexcretions, according to the following equation:

Ll = 190nu + 415nf − 250 (1)

where Ll is the leaching loss (kg N ha−1) from thearea receiving multiple excretions,nu the number ofurinations andnf the number of defaecations.

5.8. SOILN simulations representing losses fromwhole field with randomly located dung and urinepatches

Overall field leaching loss levels were determinedby combining the effects of a sample of 1000 sub-plots(out of 200,000 ha−1) with dung and urine patches ran-domly allocated in terms of position and timing. Thisshowed that when nitrate leached from different areaswas mixed up in the drainage system, overall lossesat each overall stocking rate were at almost the samelevel as those given by assuming a spatially even appli-cation of urine and faeces at that stocking rate (Fig. 1andTable 2). Thus no additional overall leaching wasfound due to localised high N applications in dung and

urine patches. The only additional losses due to spa-tially variable application rates was that arising due tocows congregating in areas of a field, as discussed inSection 5.2.

5.9. Spatial and temporal variability of soil N poolsduring and after grazing

The simulations showed a large variation in the in-organic (nitrate and ammonium) N pools in relationto time of year, as measured experimentally byWhiteet al. (1987). The spatial variability of final values ofsoil inorganic and organic N pools at the end of 10-yearSOILN simulations was investigated for the scenariosrepresenting cows congregating in an area of a field.The main effect was on the organic humus pool, whichtended to build up to a high level in the congregatingarea. This is in agreement with experimental resultspresented byFranzluebbers et al. (2000), who mea-sured a decline in organic C and N with increase indistance from shade or water sources in field whichhad previously been grazed.

6. Conclusions

The weather-driven simulation models SOILN andMACRO representing soil nitrogen processes, with pa-rameter values selected and tested in previous stud-ies related to livestock agriculture, have been appliedto the study of environmental impacts of grazing ani-mals which are exacerbated by spatial concentrationsof excreta in certain areas. The main environmentalimpacts considered here are leaching of nitrate andammonium, for which the calibrated models have beentested in previous studies at a number of sites in Scot-land. Some predictions of denitrification N losses fromSOILN simulations have also been presented for com-pleteness, but with less confidence than for the leachedN loss predictions as the model has not been testedfor denitrification at the Scottish sites. The modellingapproach has provided the opportunity to test certaineffects such as the influence on nitrate leaching of thepresence compared to the absence of grazing animals,and the comparison between leaching under grazingwith that following slurry spreading on fields cut forsilage. In contrast, it was more difficult to attribute thecauses of high nitrate leaching losses in experimental


data such as that fromHooda et al. (1998), where ashort period of autumn grazing followed slurry spread-ing on cut grass fields.

The following main water pollution effects havebeen demonstrated by the simulations.

1. Rapid transport of ammonium from urine throughsoil macropores has been shown to cause leachingof over half of the ammonium N during the firstday or two following deposition. This only ariseswhen ground is very wet, which has the implica-tion that the grazing season should not be extendedinto the late autumn. If grazing is to be extendedinto the autumn, there are substantial benefits fromrestricting it to days with reasonably dry groundconditions, i.e. avoiding grazing during or follow-ing spells of wet weather.

2. The effect of cows congregating in an area of afield, for shade, shelter, feeding or drinking, is togive a substantial increase in nitrate leaching lossesover that area, and a modest increase in losses whenaveraged over the whole grazing area. There are en-vironmental benefits from excluding livestock fromareas adjacent to watercourses, to prevent high ni-trate concentration drainage water from such areasreaching watercourses without being diluted by wa-ter from field areas with lower stocking densities.

3. High nitrate leaching losses occur during graz-ing in late summer, or if grazing is extended intolate autumn. These can be attributed to deposi-tion of N after the period of rapid grass growthso the crop is unable to extract much inorganicN and this remains in the soil to be leached overthe winter period. In general, leaching losses arehigher from grazed fields where a significant pro-portion of N is deposited at this non-optimumtime, than for fields receiving slurry applicationsand cut for silage since slurry can be appliedduring or before the period of rapid crop uptakeof N.

4. The effect of concentration of excreta in patches ofurine and dung is to increase nitrate leaching lossesfrom those areas, particularly if overlapping urineand/or dung patches arise within one grazing sea-son. However, when averaged over the whole fieldarea, leaching losses were similar to those foundby assuming even spreading at the same overallstocking rate.

Overall, the forms of pollution most damaging to theenvironment due to spatially non-uniform excretion bygrazing animals, appeared to be leached ammoniumfrom urine transported by macropore flow, and leachednitrate both due to cows congregating in certain areasof the field and deposition when N could not be utilisedas a nutrient by growing plants.

Acknowledgements

The author wishes to thank Professors Per-ErikJansson and Nick Jarvis of the Swedish Universityof Agricultural Sciences for assistance with using theSOIL, SOILN and MACRO models in this and pre-vious studies. Also, colleagues who collected data atthe SAC field sites used to calibrate the models, andDr. Lianhai Wu who developed the linkage betweenSOILN and the grass growth model. The ScottishExecutive Environment and Rural Affairs Departmentprovided funds to carry out the work.

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