prototype farm scale faecal indicator budget model -...

Prototype Farm Scale Faecal Indicator Budget Model Steven Anthony1 and Katrina Morrow Environment Modelling Group, ADAS UK Ltd. 1Corresponding author at: ADAS UK Ltd, Woodthorne, Wergs Road, Wolverhampton, WV6 8TQ. 7th November 2011 A contribution to Defra project WQ0111: Faecal Indicator Organism Losses from Farming Systems (FIO-FARM) 1st January 2007 to 31st March 2011

Abstract The mobilisation of pathogens in surface runoff and drainage from agricultural land represents a risk to drinking, recreational and shellfish harvesting waters. Mitigation of this risk requires the targeting of agricultural management options at the sources and locations on farm where they are most likely to have an impact. This requires an explicit and quantitative apportionment of the contaminant risk. Faecal indicator organisms are used as surrogate measures of the risk of pathogen contamination, and form the basis of numerical microbial water quality standards. This document reports on the development of a quantitative methodology for apportioning the relative risk of faecal indicator organism inputs to surface waters from agricultural sources. The methodology has been designed to apply at farm scale, using site-specific data, and also to explicitly represent the variability in source apportionment that occurs between neighbouring farms due to differences in livestock and manure management decisions. The methodology is demonstrated by the development of a prototype source apportionment tool in a spreadsheet that summarises average annual losses. The risk apportionment methodology utilises a number of sub-models of indicator bacteria survival and mobilisation, to explicitly represent the seasonal inputs to and runoff from each source area on a farm: septic tanks; hard-standings; roofs of farm buildings; farm tracks; fording and loafing in streams; storage and spreading of managed manures; and excreta at grazing. The relative risk associated with each source area is affected by uncertainty in characterising microbial source strengths and parameterising survival and mobilisation processes. For example, increasing the die-off rate of indicator bacteria in manure storage increases the relative importance of bacteria mobilised from fresh excreta deposited during grazing. Model parameter ranges are based on experimental data in the published literature (for example the half-life of indicator bacteria and sediment adsorption characteristics) and on measurements taken during the field experimentation phase of this project. Additional variability in the source apportionment is associated with the livestock and manure management decisions made by individual farms. As an example, losses by farm type, will be different according to whether manure is managed as slurry or farm yard manure and where animals are given direct access to streams for drinking water. The prototype source apportionment tool samples national census data and surveys of farm management systems (such as for the proportions of farms with open gathering yards or livestock access to watercourses) to explicitly represent the effects of different management decisions. The relative source apportionment on individual farms or a randomly sampled population of farms can be reported. We do not recommend that the methodology be used as an absolute predictor of the indicator load, but rather that the model output represents the relative proportion of risk associated with the different source areas on a farm. The approach could be utilised within a policy framework to assess the effectiveness of available mitigation methods, and the relative contributions of different farm types at catchment scale. This proposal is supported by a brief review of the effectiveness of relevant on-farm mitigation methods. The methodology is illustrated by the results for individual grazing animal types in England and Wales. The prototype tool could be developed further by integrating functions to predict in-stream faecal indicator concentrations for comparison with monitoring data, and by development of a more sophisticated sampling of the underlying probability distributions describing the parameter uncertainty and the variability between farms.

2FIO-FARMADAS UK LtdFIO-FARMADAS UK Ltd

Contents

Section Page 1. Introduction 5 1.1 Approaches to Modelling 5 1.2 Model Structure 9 2. Indicator Burden 12 2.1 Excreta Production 12 2.2 Faecal Coliform Concentrations 13 2.2.1 Literature Measurements of Faecal Coliform Concentrations 13 2.2.2 Project Measurements of Faecal Coliform Concentrations 15 2.2.3 Recommended Faecal Coliform Concentrations 16 3. Livestock Management Practices 18 3.1 Housing and Grazing Regime 18 3.2 Travel Time to Milking Parlour 18 3.3 Time Spent on Hard-Standings 20 3.3.1 Frequency of Yard Cleaning 21 4. Manure Management Practices 22 4.1 Manure Management System 22 4.2 Timing of Manure Spreading 22 4.3 Slurry Storage and Microbial Survival 22 4.3.1 Cattle Slurry 23 4.3.2 Pig Slurry 25 4.4 Solid Manure Storage and Microbial Survival 26 4.4.1 Cattle Manure 26 4.4.2 Pig Manure 27 4.4.3 Poultry Manure 27 5. Microbial Survival Following Spreading to Land 29 5.1 Literature Measurements 29 5.2 Recommended Rates 29 6. Methodology for Quantifying Point Source Losses 31 6.1 Runoff from Hard-Standings 31 6.1.1 Literature Measurements of Faecal Coliform Concentrations 31 6.1.2 Project Measurements of Faecal Coliform Concentrations 32 6.1.3 Hard-Standing Runoff Model 32 6.1.4 Yard Connectivity 35 6.2 Runoff from Roofs 35 6.3 Runoff from Manure Storage Facilities 36 6.3.1 Slurry Storage 36 6.3.2 Solid Manure Storage 37 6.4 Septic Tanks 38 7. Methodology for Quantifying Diffuse Source Losses 39 7.1 Losses by Direct Deposition 39 7.1.1 Stream Crossing 39 7.1.2 Direct Access for Watering 40 7.2 Runoff from Farm Tracks 41 7.3 Field Runoff and Drainage 42 7.3.1 Literature Measurements of Faecal Coliform Losses 42 7.3.2 Modelling Approach 44


Section Page 8. Integrated Faecal Indicator Budget Model 49 8.1 Modelling Approach 49 8.1.1 Farm Properties 49 8.1.2 Livestock Properties 50 8.1.3 Indicator Loss Coefficients 51 8.1.4 Model Output 51 8.2 Source Apportionment Results 52 8.3 Empirical Model Verification 56 9. Effectiveness of Mitigation Options 58 9.1 Housing Options 59 9.2 Steading Options 59 9.3 Manure Storage Options 61 9.4 Manure Spreading Options 62 9.5 Grazing Options 64 10. Discussion of Uncertainty 69 11. Conclusions 71 11.1 Recommendations for Further Work 72 12. References 73 Appendix A – Further Information on Faecal Indicator Organisms 86 Appendix B – Beta Distribution 89


1. Introduction Human enteric pathogens represent a public health risk. Transmission routes include the consumption of contaminated food and drinking water, and the unconscious consumption of water during recreational activity (Jones et al., 2002). Faecal indicator organisms (FIOs), principally coliform organisms and enterococci, are used as surrogate measures of infection risk as they indicate the presence of more dangerous enteric pathogens from faeces such as Campylobacter, Cryptosporidium parvum and Escherichia coli O157. These bacteria are excreted by all warm-blooded animals and generally exhibit rapid die-off outside the body. Faecal indicator organisms include the genera that originate in faeces – Escherichia – as well as genera that are not of faecal origin but are generally used to indicate an environmental pathway contaminated with faecal waste which may at some time be contributed to by a pathogen carrier and thereby represent an actual health risk (Kay et al., 2007). Investigations of diffuse nutrient and organic pollution of rivers have indicated that the direct excretion and spreading of livestock manures to land are sources of faecal pollution, with losses mainly attributed to surface runoff and sub-surface preferential flow pathways (Chadwick and Sen, 2002). Analyses of pathogen prevalence in animal manures have also indicated that this source represents a potential health risk to water supplies and bathing waters in the United Kingdom. Chapman et al. (1997), for example, reported the percentage prevalence of Escherichea coli. O157 in the faeces of dairy cattle as 16%, in beef cattle as 13%, and in poultry as 2%. Hutchison et al. (2005) reported the percentage prevalence of Campylobacter, Cryptosporidium parvum and Escherichia coli O157 in fresh cattle manure in the range 5 to 13%, and in fresh sheep manure of 20 to 29%. Policy developments driven by the Water Framework Directive have focussed attention on the need for quantitative information on the fluxes of indicator bacteria, and for identifying the relative importance of different sources (Kay et al., 2007). As a consequence of limited existing datasets on fluxes from agricultural sources and the effectiveness of mitigation methods from the United Kingdom, computer modelling is proposed as a means of building a synthesis of existing understanding, and enabling an analysis of potentially effective mitigation options. However, computer modelling is also limited by the available datasets, and it is necessary that the structure of any model is sensitive to the uncertainties in measurements and process understanding. Where a model is calibrated to fit limited observed data care must be taken that the parameter values are physically plausible and the differences between similarly performing parameter sets are well understood, else future application to other datasets may result in unexpected interactions (Oliver et al., 2009). 1.1 Approaches to Modelling The modelling of microbial pollution at farm and catchment scale and the associated uncertainties were reviewed by Pachepsky et al. (2006), Jamieson et al. (2004), and Oliver et al. (2009). A distinction can be made between plot, hill slope or on-farm, and catchment scales. Plot and hill slope scale modelling is concerned with estimating the survival and release of pollutants in surface runoff and drainage (see, for example, Guber et al., 2011), and the transport to and retention at edge of field (see, for example, Kouznetsov et al., 2007). Although a substantial body of experimental data exists on the survival, release and transport of microbial pollutants at plot and field scale, there is relatively little data against which to validate structural assumptions and calibrate model parameters. Pachepsky et al. (2006) note that


important components of the microbial mass balance for model development, such as hydrologic observations, are often omitted in observations or not reported. The variability of microbial measurements can also hinder testing of all but the simplest structural assumptions for processes operating over longer time periods. Deeks et al. (2005), for example, report coefficients of variation (CV) for faecal indicator concentrations in drainage ranging from 100 to 400% from three experimental field sites in the United Kingdom. Fundamental limitations exist in the knowledge base from the small scale experimental and modelling work to date. For example, in a modelling study of the relative risk of pathogen load from fresh excreta versus spread manure, McDowell (2006) concluded that a quantitative assessment could not be made. In this study, it was recognised that whilst the loss of microbial pollutants from managed manure spread to land might be expected to be greater than from an equivalent load in excreta due to the more even spread across the soil surface and greater interaction with runoff1, there is also likely to be greater die-off through exposure to radiation and desiccation in the spread manure – limiting the potential for release into runoff - whilst excreta in natural cow pats may form a protective crust (see, for example, Sinton et al., 2007). In addition, natural cow pats may also be more frequently deposited in riparian areas or on compacted soils around field gates and feeders, contributing to enhanced runoff risk. Uncertainties also exist in the most appropriate representation of microbial survival, and especially of the potential for re-growth immediately post defecation; the differences in behaviour between pathogenic and indicator organisms; sediment associations; and the spatial and temporal variability in hydrological pathways (Oliver et al., 2009; 2010; Jamieson et al., 2004). The uncertainty in process representation, and the relative importance of different sources, has not however precluded the development of mechanistic models for application at farm and catchment scale. Tools operating at a farm scale include MWASTE (Moore et al.,1989) and ECOLI (Ling et al., 2006). Muirhead et al. (2011) also developed a farm scale model, using a monte-carlo simulation approach, to calculate the relative importance of different farm sources of faecal indicator organisms to streams from dairy farms. The principal sources under base flow conditions were assumed to be direct inputs (loafing and fording of a stream) and discharges from a hard standing area. The model was constructed with estimates of the proportion of herd faeces deposited directly in a stream, if they have access to the stream, the proportion of days that the herd has access to a stream, the proportion defecating directly on crossing a stream, and the proportion crossing a stream to be milked. The estimates were based on McDowell et al. (2008); Wilcock et al., (1999) and Davies-Colley et al. (2004). The model did not consider losses from managed manures, but did represent the effects of irrigation of dirty water collected from the milking parlour and gathering yard. The model was developed to mix discharges with stream flow, with a representation of in-stream attenuation. Catchment scale mechanistic model frameworks that have attempted to represent the key stages, including in-river processes, to deliver estimates of faecal indicator concentrations at the mouth of larger river catchments have included the Coliform Source Apportionment Tool (Rose et al., 2003), an application of the MACRO model to colloid mobilisation (McGechan et al., 2008), the Soil and Water Assessment Tool (Sadeghi and Arnold, 2002; Parajuli et al., 2009), a spatially explicit model of pastoral hill-country in New Zealand (Collins and Rutherford, 2004), and further catchment tools developed by Haydon and Deletic (2006) and Tian et al. (2002). Catchment 1

Assuming a typical dairy cow pat area of 0.05 m2 (McDowell, 2006) the cow pat equivalent of a 20 to 30 t ha-1 manure application would cover only 4 to 6% of the field area.


scale efforts to date have, however, been described as rudimentary (Kay et al., 2008). The complexity of pathways and processes and inherent uncertainty associated with the development of farm and catchment scale models have also encouraged the development of more qualitative farm-scale risk assessment tools (see, for example, Oliver et al., 2009; Goss and Richards, 2008); export coefficient tools that relate indicator export to summary indices of catchment land use (see, for example, Kay et al., 2008); and syntheses of the literature on measured indicator concentrations to calculate the potential load from different source areas (see, for example, Wilcock, 2006). In the risk assessment developed by Oliver et al. (2009) a combined quantitative-qualitative approach was used, whereby quantitative assessments of indicator inputs and survival were used with qualitative assessments of environmental and socio-economic risk factors affecting transport to a watercourse and the farm management for the control of pollution. Goss and Richards (2008) have stated that there is a need for further development of indicator frameworks that allow semi-quantitative risk assessments until sufficient data are available for a full quantitative analysis. The methodology developed in this report aims to produce an index based system that utilises simple mechanistic models and the uncertainty in microbial source strengths and management practices, to assign relative risks to pre-identified source areas. It is an intermediate approach between the semi-quantitative indicators and the detailed mechanistic modelling approach. Simple relationships incorporating the principal environmental variables affecting microbial survival and mobilisation at a farm scale are used to represent the individual sources and sources areas on a farm: septic tanks; hard-standings; roofs of farm buildings; farm tracks; fording and loafing in streams; storage and spreading of managed manures; and excreta at grazing. Comparisons can be made between individual animal types and farms, or a catchment scale risk index developed through comparison of the relative risks on the different management systems present in a catchment. We have explicitly recognised the considerable uncertainty in characterising bacteria source strengths and parameterising survival and mobilisation processes by application of conceptual models with few parameters in a stochastic framework. The methodology also samples the variability in management decisions between farms. The relative risk associated with each source area on a farm is affected by uncertainty in characterising microbial source strengths and parameterising survival and mobilisation processes. For example, increasing the die-off rate of indicator bacteria in manure storage increases the relative importance of bacteria mobilised from fresh excreta deposited during grazing. Model parameter ranges are based on experimental data in the published literature, such as for the half-life of indicator bacteria and sediment partitioning, and on measurements taken during the field experimentation phase of this project. A second source of variability in the source apportionment is the livestock and manure management decisions made by individual farms. For example, the choice whether to manage manure as slurry or farm yard manure, or whether to give animals direct access to streams for drinking water, affects the die-off of bacteria during manure storage and the potential for direct inputs to streams without any die-off. The source apportionment methodology therefore also samples national census data and surveys of farm management systems (such as for the proportions of farms with open gathering yards or livestock access to watercourses) to explicitly represent the effects of different management decisions. The relative source apportionment on individual farms, or a randomly sampled population of farms can be reported.


As a consequence, we recommend that the model output is used as an index of relative risk associated with each source area, rather than an absolute prediction of the indicator load. The approach is similar in concept to the agro-environmental indicators of hygienic pressure developed by Bigras-Poulin et al. (2004) but with the addition of uncertainty analyses and explicit representation of additional source areas associated with the farmstead. Any successful farm risk model needs to characterise losses from a number of source areas, each with uncertain input and runoff characteristics:

• Farmstead hard-standings; • Fording and loafing in streams; • Farm tracks; • Field grazing area; • Manure spreading area; • Manure storage; • Septic tanks; • Building roofs;

The methodology reported in this document details the development of a quantitative risk index for each area, using as an example the faecal indicator burden associated with individual animal types in England and Wales. The methodology is demonstrated by the development of a prototype source apportionment tool in a spreadsheet. The document is organised into a number of discrete sections: Faecal Indicator Inputs and Survival

An estimate of the total number of faecal indicator organisms shed by farm livestock in excreta (Section 2);

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A review of livestock management practices affecting the locations and timing of faecal indicator inputs (Section 3); A review of the manure management practices affecting the locations and timing of faecal indicator inputs, and a review of faecal indicator die-off in storage (Section 4) A review of faecal indicator die-off following excreta deposition and manure spreading to land (Section 5)

Faecal Indicator Mobilisation

Methodology for quantifying faecal indicator losses from point sources on farm (Section 6) Methodology for quantifying faecal indicator losses from diffuse sources on farm (Section 7)

Farm Scale Modelling Results

Prototype tool for farm scale calculation of faecal indicator losses, source apportionment for individual animal types, and verification against monitoring data (Section 8);

Scoping the Impact of Mitigation Methods

Review of relevant mitigation methods and estimation of their effectiveness from literature and modelling (Section 9)


The report concludes with recommendations for further work and development of the prototype modelling framework (Sections 10 and 11). 1.2 Model Structure The model risk calculation is a composite of quantitative indices of faecal indicator source, survival, mobilisation and delivery. At the highest level, the number of livestock present and the number of faecal indicator shed in excreta each day determines the indicator source. Calculation of survival and loss then requires an explicit representation of each potential source area on a farm. Figure 1.1 illustrates these source areas. On each source area faecal indicator are added to the system in fresh excreta or managed manure, and are then subject to survival and loss. Survival is represented by first-order kinetics and is dependent upon ambient temperature and the time elapsed in storage or before mobilisation in runoff. Losses occur in surface runoff and sub-surface preferential flow, and are dependent upon rainfall and soil type. Simple conceptual relationships are used to represent the interactions between timing of inputs and runoff events. Delivery is affected by farm infrastructure factors that may reduce loss, including capture of yard runoff and fencing of fields to exclude livestock from watercourses. Critical to the quantitative assessment of risk are data on the time spent by livestock on each source area; the farm infrastructure (such as stream crossings and yard areas); the type and duration of manure storage; and the timing of manure spreading. These system data are primarily responsible for the differences in risk between different farm types, such as between farms with grazing cattle and sheep and farms with housed pig and poultry. The type of animal housing and the duration of the housing and grazing periods for farm types are defined by national stratified surveys of livestock management (Defra, 2006). The proportions of manure managed as solid or slurry, the duration of storage, and the timing of spreading are derived from national stratified surveys of manure management practice (Smith et al., 2000; 2001a; 2000b). The types of hard-standing present, their cleaning frequency, and their connectivity are defined by a national stratified survey of open hard-standings (ADAS 1999; Defra project WA0523). Stream crossings and accessibility whilst at grazing are defined from regional studies of farm practice (see, for example, Aitken et al., 2004). Further detail of these data is given in Sections 6 and 7. All data collated is intended to be typical of practice in England and Wales. The structure of the risk model encompasses the issues of source strength, transfer, connectivity, and infrastructure contributing to the direct physical risk of indicator loss from land to water identified by Fish et al. (2009; Table 1). Expert consultation organised by Fish et al. (2009) has also identified that ‘access to technical grants for waste management’; ‘participation in agri-environmental schemes’; and whether practices were embedded in ‘organic farming’ regimes were also likely to affect risk. However, we argue that these drivers relate to the potential for implementation of mitigation methods and do not affect the base risk, with the exception of organic farming. Potential mitigation controls on indicator loss are discussed in Section 9. There is no explicit representation of the indirect drivers of risk in the base loss model (grouped as income and resources; knowledge and affiliations; non-agricultural activities; and farm structure and trajectory). Critical source areas, such as compacted soils around watering troughs and feeders or riparian areas are also not explicitly represented by the model calculations. The influence of soil compaction can, however, be represented implicitly through the


selection of an appropriate soil type. The influence of topography and the spatial structure of the landscape also cannot be represented by the model. Increasing slope increases the likelihood of surface runoff crossing any landscape barriers and as such attention to these areas could bring about a significant change in the predicted indicator loss. The indicator tool that has been developed sacrifices the detail of spatially explicit modelling at field scale for the benefit of a whole farm perspective on the relative importance of different types of source area, and the variability that can occur between farm systems. The next section describes the inputs to the prototype loss model and the processes inherent in characterising the losses from different source areas. It begins with a discussion of the uncertainty in identifying the indicator burden associated with different livestock types.


LivestockHousing

Loafing andFeeding Yard

Gathering andLoading Yard

Yard Storage

Parlour andHandling Yard

Slurry Tankand Lagoon

Field andSteading Heaps

Farm Track andStream Crossing

Grazingat Pasture

StreamLoafing

Spreading Manure to Arable

SpreadingManure to Grass

Steading Practice

Housing Practice

Grazing PracticeSpreading Practice

Storage Practice

Factors:• Manure Type• Housing Duration• Housing Temperature• Manure Removal Frequency

Controls:• Antibiotics and Vaccination• Reduce Animal Stress• Dietary Manipulation

Factors:• Timing of Grazing • Grazing Duration• Ambient Temperature• Soil Hydrology

Controls:• Timing of Grazing• Riparian Filter Strips• Treatment Wetland• Stream Bridging• Stream Fencing• Water Troughs

Factors:• Manure Type• Manure Volume • Storage Duration• Storage Temperature

Controls:• Batch Storage• Acid or Liming Additives• Aeration or Digestion• Composting

Factors:• Building Roof Area• Cleaning Frequency• Ambient Temperature• Duration of Use

Controls:• Roofing Yard Area• Dirty Water Separation• Runoff Capture• Treatment Pond

Factors:• Timing of Spreading• Ambient Temperature• Soil Hydrology

Controls:• Timing of Spreading• Manure Incorporation• Treatment Wetland• No Spread Zones• Filter Strips

Figure 1.1 Schematic of the key source areas explicitly represented by the faecal indicator loss model, grouped by types of farm management practices. For each group of source areas, the key factors affecting indicator loss and a list of potential control mitigation methods are given.

2. Indicator Burden The indicator burden is defined in the model as the total number of indicator bacteria excreted by an animal each year. This is calculated as the product of the total mass of faeces produced and the average indicator concentration for each livestock type. For this project, we have developed our prototype loss tool based on estimated faecal coliform (FC) concentrations in livestock excreta. The majority of faecal coliform indicator bacteria are Escherichea coli. (EC). Appendix A provides information on the relationship between FC and EC, and also on the concentration of faecal streptococci (FS). These faecal indicator organisms are the most commonly used surrogate measures of infection risk, and are the basis of numerical microbial standards for drinking, recreational and shellfish harvesting waters under EU Directives (Edberg et al., 2000). 2.1 Excreta Production Standard volumes of excreta were taken from Defra Project WT0715 (Cottrill and Smith, 2010; Tables 26-27). These are reproduced for the livestock types of interest in Table 2.1 (below). In assessing the indicator load from excretal deposits, it is important to recognise that the pressure comes from the faecal material, and therefore that only the proportion that is of faecal origin needs to be considered. Values for the proportion of excreta produced as faeces and urine by cattle and sheep were taken from a number of studies from the literature.

Table 2.1 Daily fresh weight (kg) of total excreta and faeces produced by ruminant livestock types (Cottrill and Smith, 2010).

Ruminant Type Total Excreta Faeces

Dairy Cows and Heifers in Milk 53 34 Dairy Heifers in Calf, > 2yrs Old 40 28 Dairy Heifers in Calf, 1-2yrs Old 40 28 Beef Cows and Heifers in Milk 45 29 Beef Heifers in Calf, > 2yrs Old 32 23 Beef Heifers in Calf, 1-2yrs Old 26 18 Bulls, > 2yrs Old 26 18 Bulls, 1-2yrs Old 26 18 Other Cattle, > 2yrs Old 32 23 Other Cattle, 1-2yrs Old 26 18 Other Cattle and Calves, < 1yr Old 20 13 Sheep – Hill and Lowland 4.2 3.1 Lambs 1.8 1.3

Values for dairy cattle were taken from data reported under Defra Project NT2003, using measurements from 100 dairy cattle on 10 farms in England (North Wyke Research, 1999). Daily average production was 27.2 kg of faeces in 12.1 events and 15.4 kg of urine in 6.9 events. The average daily ratio of faeces to urine weight was 1.8 for milking cows and 2.4 for dry cows. Similar values were reported by Haynes and Williams (1993) in a review of the literature that suggested that dairy cattle on average defaecate12.8 times per day and urinate 10.2 times per day and pass 2.1 kg of faeces and 1.9 litres of urine on each occasion giving a total daily excretal output of 46.3 kg. The average daily ratio of faeces to urine weight was 1.4 for dairy cattle.


Phillips et al. (2003) reported the urine volume from 20 adult Welsh Mountain sheep to be in the range 1.24 to 1.56 litres per day, increasing with the addition of salt to the concentrate or forage. Using the daily excretal volume reported by Cottrill and Smith (2010) for hill ewes and lambs of 3.3 kg d-1, then the equivalent ratio of faeces to urine for minimal salt content is 2.7. Daily faecal load estimates for cattle and sheep are shown in Table 2.1. Values for cattle were extrapolated from the ratio of faeces to urine reported in the NT2003 study for dairy animals. Values for sheep were evaluated from the ratio obtained in the Phillips et al. (2003) study. Variability in the input data is considered for most parameters within the prototype loss model. The excretal volume is, however, normally defined as a fixed value (as specified in Table 2.1), with all the variability in the indicator burden introduced through the bounds on the concentration of indicator organisms per unit volume. This assumption is considered reasonable, as measured variability in excretal volume is small compared to the variability in the measurements of indicator organisms per unit source material. 2.2 Faecal Coliform Concentrations The concentration of indicator organisms in faeces varies with diet, age and condition of the animal. The variability is illustrated by the results of Muirhead et al. (2006) who measured Escherichea coli. concentrations in fresh cow pats over a period of 13 months from the same herd of mixed age dairy cows in New Zealand. The concentrations in the individual cow pats ranged from 9.7 MPN g-1 to 1.9×107 MPN g-

1 dry weight (n = 39).2 The following sections detail some of the observations in the literature to demonstrate this variability and the difficulty in assigning absolute values to coliform concentrations. Many of the measurements cited below are for Escherichea coli. which represents a subset of faecal coliform bacteria frequently presented in the literature. Although Escherichea coli. is not directly modelled in the budgets calculated within this report, a similar methodology with appropriate die-off rates could be utilised for this organism and loading values are thus reviewed as both support of the faecal coliform assessment and for future reference (see also Appendix A). 2.2.1 Literature Measurements of Faecal Coliform Concentrations Surprisingly few publications report consistent measurements of indicator concentrations in the fresh faeces of a range of livestock types. The majority of studies report concentrations in a single source material used for microbial survival and mobilisation studies. Avery et al. (2004), for example, reported Escherichea coli. concentrations of 50.1×106 cfu g-1 in fresh cattle faeces and 38.9×106 cfu g-1 in fresh sheep faeces; Chadwick et al. (2008) reported Escherichea coli. concentrations of 0.06×106 cfu g-1 in fresh beef cattle faeces and 1.64×106 cfu g-1 in fresh sheep faeces; and van Kessel et al. (2007) reported faecal coliform concentrations in the range 0.16 to 0.66×106 cfu g-1 in fresh beef and dairy faeces. In contrast, Cox et al. (2005) measured indicator concentrations in the faeces of a range of animal types from the Sydney watershed, Australia. Faecal coliform concentrations in calf faeces were in the range 0.58×106 to 830×106 cfu g-1; in adult

2 MPN represents the ‘most probable number’. Assessment of MPN requires a different method of laboratory analysis and though equivalent in units to ‘colony forming units’ (CFU), values measured using the two techniques often demonstrate high deviation. As MPN is based on a probabilistic measure, it tends to be associated with higher variability compared with CFU.


cattle faeces were in the range 1.3×103 to 8.5×106 cfu g-1; and in sheep faeces were in the range 0.1×106 to 190×106 cfu g-1. The median concentrations were 3.0×106 cfu g-1; 0.18×106 cfu g-1; and 0.66×106 cfu g-1 respectively. Nine samples were taken for each type of livestock. Cox et al. (2005) also reported that faecal coliform concentrations in pig faeces were in the range 5.8×105 to 4.1×108 cfu g-1; and in poultry faeces were in the range 1.1×106 to 9.5×108 cfu g-1. The median concentrations were 7.1×106 cfu g-1; and 1.1×108 cfu g-1 respectively. Weaver et al. (2005) measured indicator concentrations in fresh and dry dairy and sheep faeces on the Snake River Plain, Idaho. The average Escherichea coli. concentration in fresh cattle faeces from pasture was 0.76×106 cfu g-1; and in fresh sheep faeces was 1.12×106 cfu g-1. Six samples were taken for each type of livestock. All reported concentrations have been re-expressed as wet-weight. The moisture content of the fresh faeces was in the range 83 to 90% for cattle and 74 to 80% for sheep (Chadwick et al., 2008; Weaver et al., 2005; van Kessel et al., 2007). Havelaar et al. (1986) reported average faecal coliform concentrations of 0.56×106 cfu g-1 in freshly voided cattle faeces; 32×106 cfu g-1in calf faeces; and 12×106 cfu g-1 in sheep faeces. Thirty samples were taken for each type of livestock and groups of 3 combined to make up 10 samples for analyses. Hartel et al. (2000) measured faecal coliform concentrations in fresh and stacked broiler litter samples from eight Georgia counties. Fresh samples were obtained within two weeks of removal of the birds from the broiler house. Ten of twenty fresh litter samples had faecal coliform concentrations in the range 1.1×103 to 4.6×106 cfu g-1, and the remainder had concentrations below the limit of detection (<1×10 cfu g-1). Of 45 stacked litter samples, 44 contained concentrations below the limit of detection. Moriarty et al. (2011) measured the concentrations of Escherichea coli. in faecal samples taken from lambs at slaughter (n = 105) and sheep at pasture (n = 220). Average concentrations were 6.04×108 cfu g-1 and 6.8×105 cfu g-1 respectively.

10,000

100,000

1,000,000

10,000,000

100,000,000

1,000,000,000

Cattle Sheep Calves Pigs Poultry

Col

iform

Con

cent

ratio

n (c

fu g

-1)

Figure 2.1 Range of literature reported measurements of median or average faecal coliform and Escherichea coli. concentrations in fresh animal excreta for different livestock types (see text for references).


Metcalfe and Eddy (1991) reported daily faecal coliform burdens for all animal types, based on excreta loads and typical concentration data reported by Reddy et al. (1991; Table 1) and Geldreich et al. (1962). The faecal coliform concentrations in pig faeces were reported as 3.3×106 cfu g-1; in poultry faeces as 1.3×106 cfu g-1; in cow faeces as 0.23×106 cfu g-1; and in sheep faeces as 16.0×106 cfu g-1. Figure 2.1 visually illustrates the variability in the reported faecal coliform concentrations cited above. The data used are a compilation of the reported medians, averages or geometric means (Cox et al., 2005; Metcalfe and Eddy, 1991; Havelaar et al., 1986; Avery et al., 2004; Chadwick et al., 2008; and Weaver et al., 2005). The reported average concentrations span at least one order of magnitude. 2.2.2 Project Measurements of Faecal Coliform Concentrations Measurements of Escherichea coli. concentrations in fresh faeces were made during the field work element of this project. For adult beef and dairy cattle the concentrations were in the range 2.7×103 to 2.4×107 cfu g-1 with a geometric mean value of 4.6×105 cfu g-1 based on a sampling of multiple pats at different locations on 21 occasions. Measurements under this project for sheep faeces had a geometric mean value of 3.6×105 cfu g-1 wet-weight based on a sampling of multiple dobs at different locations on 12 occasions, with a range of 2.0x103 to 4.9×106 cfu g-1. Measurements of Escherichea coli. concentrations in fresh pig manure averaged 2.2×106 cfu g-1 (n=2). Measurements of fresh poultry litter ranged from 7.3x105 to 1.5x107 cfu g-1 (n=5) with geometric mean 4.3x106 cfu g-1. Figure 2.2 summarises the range of measured concentrations for each livestock type.

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

Cattle Sheep Pigs Poultry

Col

iform

Con

cent

ratio

n (c

fu g

-1)

Figure 2.2 Geometric average and range of measured Escherichea coli. concentrations in fresh animal faeces, measured under this project at various farm locations. Measurements of faecal coliform concentrations in fresh excreta were not made during this project. However, measurements of both faecal coliform and Esherichea coli. concentrations were made for leachate and stored manures. For leachate from stored pig slurry, Escherichea coli. accounted for 11 to 100% (with a geometric average value of 58%; n = 35) of the measured total faecal coliform population. And for stored solid manure, Escherichea coli. accounted for 15 to 99% (with a geometric average value of 68%; n = 134) of the measured total faecal coliform population.


2.2.3 Recommended Faecal Coliform Concentrations Based on the literature and measurements made during this project, estimates of typical faecal coliform concentrations were made for the faeces of each livestock type. These estimates took account of the consistent reporting of greater indicator concentrations in calf faeces compared to adult cattle, and in cattle faeces compared to sheep (Cox et al., 2004; Weaver et al., 2005; Metcalf and Eddy, 1991; and Chadwick et al., 2008). For this prototype modelling framework, we have therefore assumed a faecal coliform concentration of 0.4×106 cfu g-1 wet-weight for adult cattle; 4×106 cfu g-1 wet-weight for calves; 2×106 cfu g-1 wet-weight for sheep; and 20×106 cfu g-1 wet-weight for lambs. When integrated with the faeces loads from Table 2.1, the daily faecal coliform burden is c. 12,000×106 cfu day-1 for adult cattle and c. 6,000×106 cfu day-1 for adult sheep. In contrast, Metcalf and Eddy (1991) reported faecal coliform burdens of 5,400×106 cfu day-1 for cattle and 18,000×106 cfu day-1 for sheep, in comparison to a human burden of 2,000×106 cfu day-1. These latter values are consistent with calculations of the Escherichea coli. burden by David Kay of the Centre for Environment and Health in Wales, and cited by Jones (2002). The cattle burden was equivalent to 2.8 humans and sheep to 9.5 humans. Table 2.2 Estimated daily faecal indicator burdens for different livestock types. See text for details of source data and assumptions.

Livestock Type Faecal Output (kg d-1)

Faecal Coliform concentration (106 cfu g-1 )

Faecal Indicator Burden

(106 cfu d-1)Dairy Cows and Heifers in Milk 34 0.4 13600Dairy Heifers in Calf, > 2yrs Old 28 0.4 11200Dairy Heifers in Calf, 1-2yrs Old 28 0.4 11200Beef Cows and Heifers in Milk 29 0.4 11600Beef Heifers in Calf, > 2yrs Old 23 0.4 9200Beef Heifers in Calf, 1-2yrs Old 18 0.4 7200Bulls, > 2yrs Old 18 0.4 7200Bulls, 1-2yrs Old 18 0.4 7200Other Cattle, > 2yrs Old 23 0.4 9200Other Cattle, 1-2yrs Old 18 0.4 7200Other Cattle and Calves, < 1yr Old 13 4 52000Sheep 3.1 2 6200Lambs 1.3 20 26000Poultry 0.115 8 920Pigs 3.5 4 14000

Our estimate of the faecal coliform burden in cattle faeces is therefore greater than reported elsewhere, and the estimate for sheep faeces is less. However, it is important to consider the considerable variability between and within studies. The data presented here comes from a variety of sources using animals kept under very different management regimes. Indicator concentrations and pathogen incidence in animals has been shown to be strongly influenced by age, diet, and management


practice. Hence average values derived from a small number of measurements on farms in the United States and New Zealand, may not necessarily be representative of the range of values that occur under United Kingdom farming practices. This is a major reason, why absolute predictions of loads must be considered unreliable. It is also worth noting that the indicative concentration we have used for sheep faeces is five times greater than the average Escherichea coli. concentration in sheep faeces measured under this project (see Section 2.2.2). We have also assumed a faecal coliform concentration of 8×106 cfu g-1 wet-weight for poultry; and 4×106 cfu g-1 wet-weight for pigs. Assuming a daily faeces production of 115 g for poultry (equivalent to the daily excretal output for layer and broiler breeders; Cottrill and Smith, 2010) gives a daily faecal coliform burden of c. 900×106 cfu day-1, and a daily faeces production of 3,500 g for pigs (based on the daily excretal output for finishers; Cottrill and Smith, 2010) gives a daily burden of c. 14,000×106 cfu day-1. In comparison, Metcalf and Eddy (1991) reported faecal coliform burdens of 8,900×106 cfu day-1 for pigs and 240×106 cfu day-1 for poultry. The modelling system will be less sensitive to uncertainties in these concentrations because of the significant die-off that occurs in pig and poultry housing and subsequent manure storage (see Section 4).


3. Livestock Management Practices The concentrations of indicator bacteria in managed livestock manures (including dirty water, slurry and farm yard manure) are generally lower than in fresh excreta as a result of die-off during storage. For example, measurements of Escherichea coli. concentrations under Defra project YOS1010 (Cracking Clays Main Project) for dairy cattle slurry, stored in tanks for periods between 1 week and 7 months before spreading, were in the range 16 to 9.1×104 cfu g-1 with a geometric mean value of value of 2.5×103 cfu g-1 based on a sampling of slurry tanks at Brimstone Farm, Boxworth Farm and Great Staughton on 30 occasions. Comparison with the concentrations in fresh excreta reported above (see Section 2.2) would indicate a loss of 99% of coliform in storage. As a result, stored slurry would present a much lower risk of microbial contamination than fresh excreta at deposited at grazing. Knowledge of the proportion of livestock excreta that is managed as manure or voided directly to farm hard standing or field areas is therefore critical to the development of a risk index. This is dependent on the proportion of time an animal spends on each source area during a year. This section details the assumptions made in apportioning excreta to different source areas on a farm based on livestock management practices. 3.1 Housing and Grazing Regime The grazing livestock year can be broadly separated into a housing and grazing regime. Under each regime, the animal will spend a proportion of each day in fields, housing, walking along farm tracks, and on various types of yard (Tables 3.1 and 3.2). The time spent in each of these areas is explicitly represented by the indicator risk model. For cattle, the numbers of days spent on the grazing regime each month are estimated from Defra surveys of farming practices. The Farm Practice Survey (Defra, 2006), for example, reported on grazing practices for 550 dairy herds and 750 beef herds in 2006, giving detail of turn out dates, dates where cattle are first left out overnight, dates that they are brought in at night, and the dates of last grazing for different stock and age categories. On the basis of this data, we have estimated the proportion of time spent on the grazing regime by month (summarised in Table 3.1). The data for sheep are taken from Hellsten et al. (2007). The typical adult dairy cow in the milking herd will spend 180 days on the grazing regime. This can vary by ±20 days between farms (Defra, 2006). The Farm Practice Survey (Defra, 2008) also reported that 60% of farms out-winter a small number of stock, generally on grass fields. Out-wintering meant that animals were outside in the field all day and all night from October to March inclusive, but may be housed occasionally. The prototype risk indicator model does not take account of this practice. 3.2 Travel Time to Milking Parlour Whilst dairy animals are on the grazing regime, the main milking herd will spend a proportion of their time walking to and from the parlour along farm tracks, on gathering yards and in the milking parlour. Therefore, the milking herd is present in the fields for only part of a typical grazing day.


Table 3.1 Summary percentage of time that livestock are on a grazing regime, by month (see text for explanation).

Livestock Type Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAdult Dairy Herd 35 100 100 100 100 100 60 Dairy Calves 15 100 100 100 100 100 30 Dairy Young Stock and Followers 25 100 100 100 100 100 100 65

Beef Steers and Sucklers 20 100 100 100 100 100 90 30 Beef Young Stock and Followers 25 100 100 100 100 100 100 25 Beef Calves 15 100 100 100 100 100 30

Upland Sheep 100 100 100 100 100 100 100 100 100 100 100 100Upland Lambs* 100 100 100 100 100 100Lowland Sheep 100 65 65 65 100 100 100 100

100

100 100 100

Lowland Lambs* 30 55 80 100 100 100 75 60* Lambs are not housed, and not present when not on the grazing regime.

Table 3.2 Summary of model assumptions regarding livestock calendar (see text for explanation).

Livestock Type Handling System Management Grazing 20-80 mins walking along farm tracks

220-300 mins on collecting/self-feed silage yards Remaining hours of the day are on field

Dairy milking cow

Housed 220-300 mins on collecting/self-feed silage yards 360 mins open feeding yards (where available) Remaining hours of the day are in covered straw/concrete yards or cattle housing with scraped

passages/slatted floors Grazing 24 hours on field Beef cow Housed 480 mins open feeding yards (where available).

Remaining hours of the day are in covered straw/concrete yards


The travel time to and from the parlour for the dairy milking herd is estimated from farm track length and walking speed. Analyses of field locations for all farms in Devon and Cumbria reveal that more than 50% of fields are within 1 km of the farm centroid, used to proxy the steading location (Figure 3.1). Hence, the walking distance from the fields to the parlour is expected to be less than 1 km. Oliver et al. (2009) reported that the track density on 3 surveyed farms in the Taw catchment was in the range 0.003 to 0.013 km ha-1. For a typical dairy farm area of 100 ha, the total track length would be 300 to 1300 m, and in broad agreement with the analysis of field locations. Experiments on three breeds of cattle in France testing the effect of distance and walking speed on milk yields, found that walking speeds (inclusive of halts and solicitations) ranged from 3 to 6 km hr-1 over distances of 3.2 to 5.6 km (d’Hour et al., 1994). Using these ranges and the range of track lengths surveyed by Oliver et al. (2009), for two parlour visits per day, the time spent by dairy cattle on farm tracks was estimated to lie in the range 20 to 80 minutes.

0

20

40

60

80

100

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Distance from Farm Centroid (km)

Perc

ent o

f Fie

lds

CumbriaDevon

Figure 3.1 Cumulative distribution of distances from individual fields to holding centroid, for farms located in Cumbria and Devon. 3.3 Time Spent on Hard-Standings Hard-standings are unroofed concrete areas used by dairy and beef cattle for loafing and feeding during the housing period, and by dairy cattle as collecting yards prior to milking during both the housing and grazing period. The collecting yards will be used on average twice a day, every day, by dairy farmers for the duration of the milking. Webb et al. (2001) and Misselbrook et al. (2001) reported on a survey of the use of hard standings by livestock in England and Wales (Defra project WA0523). This survey reported 52 of 90 dairy farms having concrete collecting areas, with the remainder presumably collected on self feed silage areas (27 of 90 dairy farms, and only used before milking) or an adjacent field. Defra project WA0523 reported an average of 128 milking cows on 91 dairy farms (ADAS, 1999). A survey of 281 commercial dairy farms in England and Wales reported an average milking herd size of 132 cows with the majority having an all year round calving pattern (Gibson et al., 2005). The dominant parlour types were herringbone (75%) and abreast (17%). Rotary and other parlour types accounted for less than 8% of the total. Detailed parlour data for 33 farms showed that the number of clusters scaled linearly with the herd size (r2 65%). A parlour serving a herd of 130 milking cows would typically have 12 clusters, and one serving a herd of 260 cows would have 25 clusters. Based on this, the parlour throughput would be in the range


51 to 72 cows per hour for the 130 milking cows, and 96 to 144 cows per hour for the 260 milking cows (DDC, 2007). The total time for a single milking of the 130 milking cows would be in the range 110 to 150 minutes. High yielding dairy cows will milk in less than 10 minutes (Mein, 1998). The time spent in the parlour is therefore a small proportion of the total time spent in the collecting yard before and after milking. The open loafing and feeding yards will also be used by the cattle during the winter housing period. Defra project WA0523 reported 48 of 90 dairy farms and 113 of 230 beef farms having uncovered loafing and feeding yards. The Defra Farm Practice Survey (2006) also reported that 38% of holdings that housed dairy cattle in a solid manure system used open concrete yards to feed livestock. Excluding time spent in milking and housing, it may be anticipated that livestock would spend the majority of the day on loafing and collecting yards during the housing period. It has therefore been assumed that dairy cows spend 3 to 7 hrs per day and all other cattle spend 6 to 10 hrs per day on the loafing and feeding yards during the housing period (see also Misselbrook et al., 2001; Table 3). Where open yards do not exist on a farm, this time will be spent on covered yards or indoors and will not contribute to the risk of runoff from the farm hard standing areas. In summary, dairy milking cows will spend 20 to 80 minutes per day walking along farm tracks and 220 to 300 minutes per day on collecting or self feed silage yards during the grazing regime, and the remainder of the time in the fields. During the winter housing regime, they will spend 220 to 300 minutes per day on collecting or self feed silage yards and 360 minutes per day on open feeding yards if they exist, else they will spend their time in covered straw and concrete yards or cattle housing with scraped passages or slatted floors. Beef cattle are assumed to spend all of their time in fields during the grazing regime. During the winter housing regime, they will spend an average of 480 minutes per day on open feeding yards if they exist, else they will spend their time in covered straw and concrete yards. Defra project WA0523 reported 133 of 240 farms with ewes had open sheep handling areas. The time spent on the handling area (lowland sheep only) was estimated to be greater than or equal to 3.5 hours, but no more than 1.5 days total per year, for shearing, dipping, lambing and worming. This assumption is founded on expert judgement (Kate Phillips, pers. comm.) on the basis that sheep are handled approximately 10 times a year in areas of Wales, and 3 to 5 times a year elsewhere, and are held on the handling area for very short periods. 3.3.1 Frequency of Yard Cleaning An important aspect of farm management is the frequency of cleaning of the yard areas where excreta and indicator organisms are deposited by livestock. A high frequency of cleaning reduces the risk of indicator organisms being present when intense rainfall causes runoff from the yard areas. The frequency of cleaning open yards was surveyed by the Defra project WA0523 (ADAS, 1999). The majority of dairy collecting yards are cleaned daily (91%) and the remainder weekly. Dairy feeding and loafing yards are cleaned daily (84%); weekly (12%); and monthly (4%). Beef feeding and loafing yards are cleaned daily (35%); weekly (33%); monthly (16%); and yearly (16%).


4. Manure Management Practices Farm management decisions relating to the type, duration of storage and timing of manure spreading to land affect the proportion of faecal indicator organisms in excreta that survive to be potentially lost in runoff and soil drainage. This section reviews the data characterising manure management practices in England and Wales. 4.1 Manure Management System During the housing period, the majority of dairy cattle in England are housed on a slurry based system (72%) and the remainder on a solid farm yard manure (straw based) system (37%) (Defra, 2006). During the housing period, beef suckler cattle are housed on either covered straw yards (83%), or slurry based scraped passages (14%) and slatted floor systems (3%) (Defra, 2006). Similar results have been reported for Wales. Anthony et al. (2011), surveyed 600 farms in Wales and reported that 66% of handled manure was managed as slurry on dairy farms, compared to 10% of cattle and sheep farms. In pig rearing systems, the majority (60%) are based on solid farm yard manure, with the remainder either on slurry or producing both types of manure (Smith et al., 2000). All outputs from poultry systems are solid litter or manure. 4.2 Timing of Manure Spreading The timing of managed manure spreading to land was surveyed by Smith et al. (2000; 2001a; 2001b). Table 4.1 summarises the results for 471 dairy, 515 beef, 576 pig, and 356 poultry farms in England and Wales. Based on these data, it was estimated that 40 to 60% of manure was spread during the summer (April to September) period. Table 4.1 Farmer estimates of the timing of managed manure applications to crops and grassland (Smith et al., 2000; 2001a; 2001b).

Percentage of Amount Spread (%) Livestock Type Manure Type Feb-April May-July Aug-Oct Nov-Jan

Beef Cattle Slurry 46 13 20 21 FYM 28 10 42 20

Dairy Cattle Slurry 40 10 24 26 FYM 40 10 25 26

Pig Slurry 27 18 35 20 FYM 17 7 56 19

Poultry Broiler 26 9 50 15 Layer 21 16 44 19

4.3 Slurry Storage and Microbial Survival The majority of farms in England and Wales store slurry in either steel tanks or earth banked lagoons (Nicholson and Brewer, 1997). Survey data exists on the storage capacity of farms, but not explicitly on the duration of storage, which must be inferred from the capacity data, and knowledge of typical farm practice. The storage capacity


for slurry was surveyed by Dauven and Crabb (1998a), Parham (1997a), and Smith et al., (2000; 2001a). 4.3.1 Cattle Slurry There is considerable variability in the type of slurry storage and capacity between farm types and within farms of the same type. Smith et al. (2001), for example, reported that 72% of dairy farms had separate dirty water storage and disposal systems. But only 23% of beef farms have separate systems. More than 25% of beef slurry is managed on farms with either no storage or less than one month storage, compared to 16% of dairy slurry. Typically an above ground circular slurry store on a dairy farm would have three months storage capacity, but would contain slurry from October until May (Nicholson and Brewer, 1997). Earth-banked lagoons containing cattle slurry will be continuously filled during the winter housing period, and emptied from April onwards. However, Dauven and Crabb (1998) reported that slurry stores were never empty during a typical year on 58% of 347 surveyed farms. The majority of above ground circular slurry stores are regularly (once a week) or occasionally agitated. The temperatures in slurry stores generally follow ambient temperatures. Table 4.2 Slurry storage capacity of dairy and beef farms, expressed as a percentage of total handled manure (Smith et al., 2001).

Manure Type

<1 month >1-2 months >2-4 months >4-6 months > 6 months

Beef Slurry 25.4 11.6 32.3 24.5 6.2 Dairy Slurry 16.0 11.0 35.0 22.0 16.0

Table 4.2 summarises the surveyed storage capacity data for cattle slurry (Smith et al., 2001). For this work, we have assumed that the slurry storage capacity can be directly related to the typical duration of storage. We also assumed that manures managed as slurry were collected on a daily or weekly basis for storage, so there was little opportunity for die-off in the animal housing. Die-off of indicator bacteria and pathogens occurs during the storage of manure. As few farms manage their manure stores as batches, the effect of die-off can be difficult to calculate as fresh material is added continuously. It is possible that direct measurements of microbial concentrations in stored manure represent the best estimate of the burden spread to land. In the first instance, however, we implemented a mass balance approach, to explicitly model the die-off process, assuming that fresh manures were added daily to the store. This allowed easier manipulation of the model to assess ‘change in manure storage practices’ as a mitigation option. To represent the impact of storage required data on the rate of die-off of coliform bacteria in storage. Die-off rate data from the literature were reviewed, and are expressed as a half-life (days) to avoid confusion between rates calculated using decimal and natural logarithms. Wang et al. (2004) measured the die-off of Escherichea coli. in small samples of dairy cow excreta held under constant environment conditions in the laboratory. The half-life at 4 oC was 6.3 days, at 27 oC was 3.5 days and at 41 oC was 2.2 days. Meals and Braun (2006) measured the die-off of Escherichea coli. in dairy slurry stored in outdoor manure tanks at ambient temperatures for up to 90 days. The half-


life at 11.3 oC was in the range 5.0 to 10.7 days, and at 18.1 oC was in the range 4.2 to 7.0 days (n = 9). Munch et al. (1987) measured the die-off of Escherichea coli. in dairy and pig slurry held in experimental tanks at temperatures representative of average summer and winter temperatures for up to 100 days. The half-life at 7 oC for aerated cattle slurry was in the range 3.0 to 3.8 days (n = 8) and at 20 oC was in the range 1.5 to 4.8 days (n = 4). The half-life at 7 oC for un-aerated cattle slurry was in the range 7.2 to 19.9 days (n = 6) and at 20 oC was in the range 3.4 to 9.5 days (n = 8). The half-life at 7 oC for aerated pig slurry was in the range 3.6 to 7.0 days (n = 6) and at 20 oC was in the range 1.5 to 3.6 days (n = 4). The half-life at 7 oC for un-aerated pig slurry was in the range 12.9 to 38.6 days (n = 5) and at 20 oC was in the range 2.8 to 4.0 days (n = 6). The half-life was significantly reduced in aerated stores.

y = 14.012e-0.0446x

R2 = 0.4758

0

5

10

15

20

25

0 10 20 30 40 5

Temperature (oC)

Hal

f Life

(Day

s)

0

Figure 4.1 Measured Escherichea coli. half-lives in dairy slurry as a function of temperature in experiments by van Kessel et al. (2006); Wang et al. (2004); Meals and Braun (2006); and Munch et al. (1987). Data is presented to show the general trend in the relationship between the half-life of the organism and the storage temperature. It should be noted that the data represents a compilation of a number of data sets in which the environmental conditions and source materials were different, such that temperature may not be the only factor explaining the differences in observed half-life. The literature die-off rate data are summarised by Figure 4.1, which illustrates the variability in the calculated half-lives between each experiment, and the general trend towards more rapid die-off at high temperatures. Based on these data, the half-life of stored slurry was estimated to be in the range 2 to 10 days at a reference temperature of 20 oC, or 4 to 22 days at an average temperature of 10 oC. Our model calculations of microbial survival used the range of potential half-lives to represent the uncertainty. The long-term annual average temperature in England is 9.4 oC and the average winter temperature is 5.8 oC (Met Office, 1971-2000). Slurry is stored in tanks and lagoons under ambient conditions, principally during the winter months for cattle, and all year for pigs. Therefore, die-off was based on an effective half-life of 6 to 31 days for cattle slurry; and 4 to 23 days for pig slurry. The majority of the half-life values cited above for coliform bacteria relate to Escherichea coli. In this report, we assume that both the magnitude and relationship with temperature of the half-life of Escherichea coli. is representative of the half-life of faecal coliforms in general. It should however be noted that in experiments by van


Kessel et al. (2006), die-off rates of faecal coliforms in small samples of dairy and beef excreta held under constant environment conditions in the laboratory, were consistently lower than the die-off rates of Escherichea coli. in the same samples (see Table 4.3). Table 4.3 Half-lives of Escherichea coli. and faecal coliform in cattle excreta stored in the laboratory under different temperature conditions. Source data from van Kessel et al. (2006).

Half-life (days) Temperature (oC) Faecal Coliform Escherichea coli. 21.1 8.6 9.8 26.7 5.5 6.7 32.2 4.2 5.5

To estimate the survival of faecal indicator organisms in storage, the probability distribution of storage duration was integrated with the feasible range of half-lives to calculate the probability distribution for the proportion of indicator organisms surviving storage. This probability distribution represented both the uncertainty in the half-lives and the variability between farms in slurry storage duration. Using the survey data on the distribution of slurry storage capacity on individual farms (Table 4.2) and assuming continuous additions to the store, the percentage of the indicator burden surviving during cattle slurry storage was calculated to be in the range 5 to 85% with an average value of 30% at an ambient winter temperature of 5.8 oC for cattle slurry. The asymmetric probability distribution was well described by a Beta distribution, which was used to describe it in the modelling framework. {Beta Parameters Ave 0.30; Std 0.20} 4.3.2 Pig Slurry Die-off of bacteria in pig slurry also occurs in the pig housing as the slurry is removed infrequently to storage before spreading to land. Parham (1997a) report that slurry from pig housing is removed daily (24%); weekly (25%); monthly (47%); and twice yearly (4%). Once removed, 16% is spread immediately and the remainder is stored for up to 9 months (Table 4.4). Integrating microbial die-off over both the housing and storage period, and assuming continuous additions to the stores, the percentage of the indicator burden surviving was calculated to be in the range 1 to 75% with an average value of 15% at an ambient annual temperature of 9.8 oC for pig slurry. The asymmetric probability distribution was well described by a Beta distribution, which was used to describe it in the modelling framework. {Beta Parameters Ave 0.15; Std 0.20} Table 4.4 Slurry storage capacity of pig farmers, expressed as a percentage of total handled manure (Parham, 1997a).

Manure Type

not stored

<1 month

>1-2 months

>2-3 months

>3-6 months

>6-9 months

> 9 months

Pig Slurry 16 11 18 13 32 4 6


4.4 Solid Manure Storage and Microbial Survival The die-off of faecal indicator organisms in solid manure storage was calculated in the same way as for slurry, but with the added complication that for manures it is frequently difficult to explicitly separate housing and storage periods for manure as bedding material is often kept in the animal housing for the entire duration of a pig or poultry cohort, or the winter housing period for cattle and sheep, before removal to temporary storage before spreading to land. Hutchison et al. (2005) reported national survey data where 69% of cattle manure stores have continuous additions of manure, and 81% of pig manure stores. Overall, 90% of manure in these stores were never turned, stirred or otherwise aerated. In the majority of situations, three to nine months of storage in housing would be typical before removal (Nicholson and Brewer, 1997). An estimated 30% of solid manure is removed from buildings and spread directly to land on dairy farms (Dauven and Crabb, 1998a). The Defra Farm Practice Survey similarly reports that 28% of solid manure is spread immediately after removal from the animal housing; 26% is stored in the open on an impermeable concrete base; and 44% is stored in the open on a field site (Defra, 2006). The average storage period for cattle solid manure was 5.5 months, with an inter-quartile range of 3 to 6 months. The average storage period for poultry litter was 6.5 months, with an inter-quartile range of 3 to 9.5 months. However, 58% of poultry litter is spread immediately after removal from the building (Defra, 2006). 4.4.1 Cattle Manure For solid dairy and beef cattle manure, our model calculations assumed that the majority (80%) would be stored in the animal housing for 6 to 9 months, and the remainder for 2 to 3 months, before removal for either immediate spreading (30%) or temporary storage for a further 1 to 6 months (Table 4.5). Table 4.5 Estimated storage duration for solid farm yard manure from dairy and beef cattle, following removal from animal housing, expressed as a percentage of total handled manure (Smith et al., 2001).

Manure Type

not stored

<1 month >1-2 months

>2-4 months

>4-6 months

>6 months

Beef FYM 30 0 10 25 25 10 Dairy FYM 30 0 10 25 25 10 The die-off of faecal coliforms in manure should proceed at a similar rate to slurry if stored at similar temperatures. Jones (1971) measured the die-off of faecal coliforms in exposed and covered dairy solid manure heap in the field. The half-life was in the range 11 to 25 days at ambient temperatures in the range 2 to 8 oC, which is comparable to the assumed 4 to 22 days at 10 oC (see Section 4.1.1). Rankin and Taylor (1969) also measured a comparable average half-life of 6.3 days for Escherichea coli. in a dairy solid manure heap. However, solid manure will also naturally compost, producing heat and enhancing the indicator die-off. This can be enhanced by turning the heap to provide oxygen to the aerobic bacteria responsible for the break down of the organic matter in the manure. The composting of solid manure will raise the temperature to 35 to 75 oC and accelerates the die-off of


indicator bacteria and pathogens. Die-off of 99.9% has been reported within one day providing temperatures exceed c. 50 oC (Grewal et al., 2006; Larney et al., 2003; Turner, 2002). The long-term annual average temperature in England is 9.4 oC and the average winter temperature is 5.8 oC (Met Office, 1971-2000). Solid manure is stored all year round. For this project, we assumed that whilst in the animal shed the solid manure will be at ambient temperatures for cattle and sheep housing; 15 to 20 oC for pig housing; and 15 to 25 oC for layer and broiler housing. The effective half-life while solid manure is housed will therefore be in the range 4 to 23 days for cattle and sheep; 2 to 15 days for pig housing; and 2 to 10 days for layer and broiler housing. When stored in the open as stacks or heaps (but not purposely composted), we have then assumed that the manure temperature will rise into the range 25 to 35 oC to give an effective half-life in the range 1 to 7 days. Using the survey data on the distribution of solid manure storage duration on cattle farms (Defra, 2006) and assuming continuous additions to the store, the percentage of coliform surviving was calculated to be in the range 1 to 6% with an average value of 3% for solid cattle manure. {Beta Parameters: Ave 0.03; Std 0.06} As a consequence of the extended periods of temporary storage in the livestock shed, and the higher temperatures achieved during heaped storage, we calculated that the average faecal indicator survival following storage was only 3% for solid cattle manure in comparison to 30% for cattle slurry. 4.4.2 Pig Manure Parham (1997a) report that solid manure from pig housing is removed daily (56%); weekly (24%); monthly (15%) and the remainder twice yearly. The manure is then stored for 2 to 6 months before spreading (Table 4.6). Whilst in the animal shed, we assumed that the manure would be held at a temperature of 15 to 20 oC, and that the temperature would rise into the range 25 to 35 oC when stored in a heap. Table 4.6 Manure storage capacity for pig farmers, expressed as a percentage of total handled manure (Parham, 1997a).

Manure Type

not stored

<1 month

>1-2 months

>2-3 months

>3-6 months

>6-9 months

> 9 months

Pig FYM 0.0 0.0 0.0 30.0 55.0 15.0 0.0 By integration across the both the shed cleaning frequency and manure storage duration data and assuming continuous additions to the store, the percentage of coliform surviving was calculated to be in the range 1 to 6% with an average value of 2% for solid pig manure. {Beta Parameters: Ave 0.02; Std 0.02} 4.4.3 Poultry Manure Parham (1997b) report that litter from layer poultry housing is removed daily (24%); weekly (36%); and at the end of the production cycle (41%) typically of 50 weeks. Once removed, 43% is spread immediately and the remainder is stored for up to 9


months (Table 4.7). Integrating die-off over both the housing and storage period, and assuming continuous additions to the stores, the percent of the indicator burden surviving was calculated to be in the range 3 to 24% for layer litter. The litter from broiler houses is all stored within the building until the end of the production cycle (100%) typically of 7 weeks. Table 4.7 Litter storage capacity of poultry farmers, expressed as a percentage of total handled manure (Parham, 1997b).

Manure Type not stored

<1 month

>1-2 months

>2-3 months

>3-6 months

>6-9 months

> 9 months

Layer / Broiler 43 0 14 7 27 3 6 Integrating die-off over both the housing and storage period, and assuming continuous additions to the stores, the percent of the indicator burden surviving was estimated to be in the range 1 to 25% with an average value of 5% for broiler litter. {Beta Parameters: Ave 0.03; Std 0.04}. The percent of the indicator burden surviving was estimated to be in the range 1 to 60% with an average value of 15% for layer manure. {Beta Parameters: Ave 0.10; Std 0.15}. The higher value reflecting the more frequent cleaning of the poultry house.


5. Microbial Survival Following Spreading to Land Estimates of the die-off rate of faecal indicator organisms following excretion and spreading of managed manures to land are required by the hard-standings and field runoff sub-models (Sections 6 and 7). Die-off of indicator bacteria after manure spreading or direct voiding of excreta is likely to be more rapid than during storage because of increased environmental stresses, including desiccation and radiation. Microbial survival increases with moisture and organic matter, and decreases with temperature, exposure to radiation, soil particle size and excessively high or low pH (see, for example, Reddy et al., 1981; Moore et al., 1988; Howell et al., 1996; Jamieson et al., 2002). A number of model formulations exist to represent the effect of these variables on the die-off process, but they were developed and calibrated for very specific conditions (Jamieson et al., 2004). The inference is that a published model formulation may not be widely applicable. For this prototype framework, we adopted the commonly used first-order kinetics model of die-off in which die-off is characterised by a microbial half-life. 5.1 Literature Measurements Reddy et al. (1981) reviewed die-off rates of indicator organisms and pathogens in soil and manure systems. The average half-life for faecal coliform in manure and soil was in the range 0.9 to 9.9 days with a geometric average of 2.0 days (n = 18); and for Escherichea coli. was in the range 1.5 to 3.0 days with a geometric average of 2.1 days (n = 12). Temperatures where reported, were generally between 20 and 30 oC. Experimental data reviewed by Moore et al. (1988) give a range of 0.50 to 26.7 days for faecal coliform for a range of temperatures and types or manure, with a geometric average of 4.3 days (n = 18). Note that these reviews are not entirely independent. Avery et al. (2004) measured average half-life for coliform bacteria in livestock faeces deposited directly to pasture. The half-life for cattle faeces was 11.4 days and for sheep faeces was 10.8 days at ambient winter temperatures. Ogen et al. (2001) monitored the die-off of Escherichea coli. in soil cores from Glencourse, Scotland, in the laboratory that had received slurry. The decline was modelled using a biphasic model with a susceptible (97%) and resistant (3%) population. The half-life of the susceptible population was 4.1 days at 6 oC and 3.3 days at 15 oC. The half-life of the resistant population was in the range 17.8 to 23.5 days. 5.2 Recommended Rates Although limited, these literature data would support an indicative reference half-life at 20 oC for faecal coliform in the range 2 to 5 days, or at an average annual temperature of 9.4 oC a range of 4 to 11 days. The method of manure application should also be considered when estimating microbial survival times. Hutchison et al. (2004), for example, reported average decimal reduction times for Salmonella, Escherichea coli. O157, Listeria and Campylobacter for both dairy cattle and slurry that were significantly longer for immediately incorporated manures compared to manures left on the surface after spreading in early winter and late spring. Chadwick et al. (2008) similarly monitored the die-off of Escherichea coli. in fresh dairy slurries spread to land in May, July and October. Where the manure was shallow injected, the number of days to return to background soil levels was 1.5 to 2.3 times the number required for conventional surface broadcasting. For this prototype modelling framework, we have assumed that managed manures are surface broadcast and not incorporated.


It is also necessary to be aware of the potential for bacterial re-growth under wet conditions. Sinton et al. (2007) measured the survival of Escherichea coli. in dairy cow pats placed on pasture. Initial increases in concentrations of up to 1.5 orders of magnitude were observed in 3 of 4 seasons. Growth was associated with re-hydration of the cow pats. The half-life of the Escherichea coli. were in the range 11.5 to 13.9 days at ambient seasonal average temperatures of 6.2 to 19.5 oC. van Kessel et al. (2007) also observed that indicator concentrations increased by as much as 1.5 orders of magnitude in the first week. Re-growth is presently represented in the modelling framework as an optional multiplier in the range 1 to 10. It is applied only to the indicator load from manures spread or deposited on the fields, farm tracks or hard standing area. It does not apply to the indicator load deposited directly into watercourses. It is not clear whether the potential for re-growth should only apply to manure that has not been stored for any significant period.


6. Methodology for Quantifying Point Source Losses This section is concerned with point sources of faecal indicator organisms on the farm, and includes runoff from farmyards and discharge from septic tanks. A simple conceptual model of losses from a farm hard-standing is presented, that is also used to calculate losses from farm tracks (Section 7). Model predictions are compared with measured concentrations from the literature where available. Published information on losses from septic tanks, roof runoff and manure storage is reviewed. 6.1 Runoff from Hard-Standings Concrete hard-standings contaminated with livestock excreta can be a significant source of microbial contamination if the yard runoff is not captured and stored. Beef and dairy cattle use open hard standing areas for loafing and feeding during the housed period, and dairy cattle use gathering yards whilst waiting for milking during the grazing period. For a single dairy cow, it may be supposed that total time spent on yard areas during the grazing regime is 4 to 5 hours (Section 3). For collecting yards, the average yard area per cow is 3.56 m2 cow-1 and for feeding yards, the average yard area per cow is yard is 5.72 m2 cow-1 (Farm Practice Survey, 2006). There is however, variability in these estimates both within the cited farm survey and between other sources in the literature (see, for example, Defra project WA0523). The average volume of excreta deposited by an adult cow is dependent on the weight, health and diet of the animal. It is assumed that 34 kg represents the volume of fresh faeces per day from a typical adult cow, and that the daily faecal indicator burden is c. 12,000×106 cfu (Section 2). If the entirety of the load deposited on the yard area were lost in rainfall and runoff from the yard, then the typical runoff concentration was estimated to be of the order 100×106 cfu 100 ml-1 for annual rainfall in the range 600 to 1200 mm, in comparison to measured concentrations in field drainage of the order 103 to 106 cfu 100 ml-1 (Vinten et al., 2004; Oliver et al., 2005; Merrilees et al., 2004). However, the actual loss is expected to be much less due to the effects of die-off, yard cleaning and the capture of yard runoff. The loss is also expected to vary considerably between farms in proportion to the period of time spent by animals on yard areas and the proportion of the herd that use the yard space. 6.1.1 Literature Measurements of Faecal Coliform Concentrations The majority of farm yard surfaces are made up of concrete and of over 100 farms considered in a Defra sponsored hard standings survey (project WAO516) over 90% were found to be in good condition (without cracks and ruts). The primary pathway is therefore surface runoff. A number of monitoring studies have found that runoff from farmyards contaminated with animal faeces represents a significant source of microbial pollution. Reyne et al. (1998), for example, monitored faecal coliform concentrations in runoff from the yards and buildings on three dairy farms in France. Concentrations at times of low and high flow were in the range 1.9×104 to 2.5×106 cfu 100 ml-1, with an average value of 3.3×105 cfu 100 ml-1 (n=45). Uusi-Kampaa et al. (2006) monitored indicator concentrations in runoff from dairy cow exercise yards in Finland. The average faecal coliform concentrations at five yards were in the range 6×103 to 7×106 cfu 100 ml-1 (n=15). Edwards et al. (2008) monitored indicator concentrations in summer storm runoff from areas of roofs and hard standings situated on four dairy and beef farms in Ayrshire, Scotland. Median faecal coliform

concentrations in runoff from hard standings were in the range 77.3×103 to 13.9×106 cfu 100 ml-1 (n=18). Concentrations ranged over 4 orders of magnitude and were highly skewed. The lowest median value was associated with a farm in which animals were not present in the farmyard. Uusi-Kamppa (2005) measured faecal coliform concentrations in water taken from open ditches, drain pipes and drain wells on 19 dairy farms in Finland. Geometric average concentrations from sites adjacent to outdoor yards were 970 cfu 100 ml-1 (n=5), adjacent to farm yards were 300 cfu 100 ml-1 (n=7), and adjacent to loose housing were 68 cfu 100 ml-1 (n=20). The majority of samples were taken in spring. Eight samples were also taken from surface runoff water from outdoor yards, giving an average result of 6,600 cfu 100 ml-1. Lewis et al. (2005) measured indicator concentrations in storm surface runoff from driveways and parking areas, drains, manure stock pile areas, pasture, manure storage systems and exercise lots for 10 dairy farms in California. Faecal coliform concentrations in exercise lot runoff averaged 3.1×106 cfu 100 ml-1 (n = 91; s.e. 1.9×106 cfu 100 ml-1). Runoff from driveways and similar averaged 0.2×106 cfu 100 ml-1 (n = 16). These can be compared to average concentrations of 0.1×106 cfu 100 ml-1 from pastures (n = 48). Measurements of faecal coliform concentrations in gutters representing roof runoff averaged only 298 cfu 100 ml-1 (n=17). Muirhead et al. (2005; 2006) measured Escherichea coli. concentrations in the runoff from natural and aritificial cow pats placed on tin trays and subject to simulated rainfall of 25 mm hr-1. Experiments investigated runoff from fresh and 2 to 30 day old cow pats. Concentrations in runoff were linearly correlated to the concentration in the cow pats (r2 90%). Only c. 10% of Escherichea coli. cells were attached to soil or manure particles in the runoff. This is generally supported by the results of Thelin and Gifford (1983) who monitored concentrations in runoff from artificial cow pats aged by up to 30 days without rainfall, and observed that the released concentrations were consistent with the generalised growth curve of a bacterial culture. Rainfall intensity was significant only for aged cow pats. Low intensity treatments delayed the on set to peak concentrations (Springer et al., 1983). 6.1.2 Project Measurements of Faecal Coliform Concentrations Measurements of Escherichea coli. in drainage water from a hard-standing were made at Thorney Abbey farm as part of the field work element of this project. Samples were collected from two drains on a loafing area where cattle congregated before milking. The farm dairy herd was 100 cows, and the yard was scraped clean twice a week. Measured concentrations were in the range 4×103 to 7.3×106 cfu 100 ml-1, with a geometric mean concentration of 7.6×105 cfu 100 ml-1 (n = 34). Samples were collected throughout the year. 6.1.3 Hard-Standing Runoff Model Hard-standing runoff was modelled using a simple conceptual model. On each day, a quantity of faecal indicator was added to the hard-standing area in proportion to the time that the cattle were present (see Section 3). The faecal indicator organisms were then assumed to die-off at a rate dependent upon ambient temperature (a reference half life of 2 to 5 days at 20 oC; see Section 5.2), and were also removed by scraping or pressure washing of the yard. The faecal indicator input was partitioned between a number of compartments, representing locations on the yard


that vary in the quantity of rainfall required to connect the yard area to a watercourse. On any rain day, the quantity of runoff from the whole yard was calculated using the Soil Conservation Service (SCS) model, with a curve number of 90 to represent the impermeable surface, as used in a number of feed lot models (see, for example, Kizil et al., 2006; Dickey and Vanderholm, 1977). The curve number of 90 is representative of compacted soil surfaces, and is therefore also appropriate for farm tracks. A curve number of 98 to 100 would be more representative of smooth and clean impermeable concrete surface, and would result in a 5 to 10 fold increase in surface runoff but a maximum 2 fold increase in the percentage of the indicator burden lost from the yards. However, the lower curve number has been used for the open yard areas in order to represent the presence of cracks, the uneven surface and the likely presence of scattered organic material that would absorb some of the rainfall. The indicator in each compartment is washed out by this runoff if the daily rainfall exceeds a critical value. The critical values are assigned separately to each compartment, using a linear model inversely related to the cumulative frequency of daily rainfall totals. Ninety percent of the yard area requires that the daily rainfall is greater than the 10th percentile rainfall total to be washed out, and ten percent of the yard area requires that the daily rainfall is greater than the 90th percentile rainfall total. This approach is intended to represent the inhomogeneous yard areas, with some areas experiencing wash-off from fairly small events, and other areas only experiencing wash-off in intense rainfall events that produce sufficient runoff to connect the whole of the yard to the exit point. In physical terms this may correspond to the bottom edge of a sloped yard, which will experience greater volumes of flow, or an area of the yard adjacent to the milking parlour, that is sheltered from the general prevailing wind direction, or perhaps an area with a pot-hole, that only ever experiences standing water, which is then evaporated off. The wash out of a compartment removes a proportion of the faecal indicator load present. This proportion was estimated to lie in the range 0.002 to 0.008 mm-1 as this gave simulated average runoff concentrations that were in the range 4×105 to 3×106 cfu 100 ml-1 for a yard cleaned daily, and were similar to the literature measurements. The yard runoff and washout model was linked to a synthetic weather generator and used to calculate average indicator losses over a 100 year period for a range of representative climate conditions, and for coliform half-lives in the range 2 to 5 days. A critical control on simulated losses was the frequency and efficiency of yard cleaning. The Farm Practice Survey (2006) indicated that the collecting yards are most often cleaned daily and feeding yards weekly. Table 6.1 summarises survey data on the frequency of cleaning from Defra project WA0523.

Table 6.1 Frequency of yard cleaning (Defra project WA0523).

Percent of Farms (%) Frequency Dairy collecting

yard Dairy feeding and

loafing yard Beef feeding

and loafing yard Daily 91 84 35 Weekly 9 12 33 Monthly 0 4 16 Yearly 0 0 16


In an investigation of ammonia emission mitigation strategies, Misselbrook et al. (2005) reported that pressure washing of a yard reduced ammonia emissions by 90% whilst scraping reduced the mass of excreta on the yard surface by 60%. Cleaning a yard area was therefore assumed to remove 60% of the indicator bacteria at risk of runoff. Tables 6.2 and 6.3 summarise the results of the hard-standing runoff model for a scenario consisting of 2×109 cfu being deposited on a 10 m2 yard area each day. The scenario is broadly equivalent to the faecal indicator input from one adult cow present for 4 hours each day. For a location with a monthly rainfall of 60 mm and a daily yard cleaning frequency, the percentage of the indicator burden lost in surface runoff is in the range 0.2 to 0.7% across the range of reference half-life (2 to 5 days) and wash out parameter (0.002 to 0.008 mm-1). Modelled indicator concentrations in the yard runoff were in the range 0.5 to 2.1×106 cfu 100 ml-1. Weighting the model output by the survey data on frequency of yard cleaning, it was estimated that between 0.2 and 1.1% of the excreta burden on the yards would be lost for dairy animals and between 0.6 and 4.0% for beef animals. Table 6.2 Modelled range of the percentage of total faecal indicator burden lost in surface runoff from farm yard areas cleaned daily, weekly or monthly. The reference faecal coliform half life was varied from 2 to 5 days, and the wash out parameter was varied from 0.002 to 0.008 mm-1.

Monthly Rainfall (mm) Cleaning Frequency

Percentage Loss (%) 40 50 60 70 80 90 100

Daily Minimum 0.08 0.13 0.18 0.23 0.29 0.35 0.40 Maximum 0.37 0.51 0.71 0.91 1.12 1.38 1.56

Weekly Minimum 0.34 0.50 0.69 0.87 1.09 1.27 1.56 Maximum 1.68 2.79 3.57 4.49 5.51 6.57 7.66

Monthy Minimum 0.48 0.77 1.01 1.30 1.64 1.93 2.34 Maximum 3.57 5.36 7.07 9.25 10.71 12.77 14.53

Table 6.3 Modelled range of the faecal coliform concentrations (106 100 ml-1) in surface runoff from farm yard areas cleaned daily, weekly or monthly. The calculations assumed a daily coliform input of 2×109 cfu over an area of 10 m2.

Monthly Rainfall (mm) Cleaning Frequency

Concentration Range 40 50 60 70 80 90 100

Daily Minimum 0.66 0.58 0.54 0.50 0.48 0.44 0.42 Maximum 2.56 2.34 2.12 1.98 1.82 1.76 1.66

Weekly Minimum 2.48 2.16 2.06 1.86 1.72 1.72 1.60 Maximum 12.76 11.58 10.68 9.70 8.84 8.42 7.96

Monthly Minimum 3.64 3.46 3.08 2.86 2.66 2.52 2.38 Maximum 26.10 23.52 20.66 19.42 17.68 16.62 15.22


6.1.4 Yard Connectivity Not all of the pollutant mobilised in a yard will reach the stream, and a connectivity coefficient is therefore also applied to represent the probability that a yard area is connected directly to a watercourse. Hatch et al. (2004) reported that 80 to 90% of runoff from farm hard standings on dairy farms and 50% of runoff on beef farms was collected in a waste store. Aitken et al. (2001) reported that 52% of the runoff from yards contaminated with faecal matter was collected in a slurry store on cattle and sheep farms in Ayrshire. In a survey of 600 farms in Wales, Anthony et al., (2010) reported that 6% of Dairy farms, 30% of Upland Cattle and Sheep; and 15% of Lowland Cattle and Sheep allowed yard runoff into a field or ditch; a further 0% of Dairy farms, 33% of Upland Cattle and Sheep; and 42% of Lowland Cattle and Sheep reported that they did not manage their yard runoff. The majority of the remainder diverted the runoff to a dirty water or slurry store. Based on these survey data, we assumed a yard connectivity coefficient of 15% for dairy farms and 50% for beef and sheep farms. 6.2 Runoff from Roofs Rainfall and water used in the cleaning of yards and machinery that is contaminated with faeces is termed dirty water. The dirty water from farmyards can be increased by drainage from building roofs that is not intercepted by guttering and downpipes. The roof runoff adds to the volume of water flowing across potentially contaminated steading areas, and may also be contaminated at source. Measurements of Escherichea coli. concentrations in direct roof runoff at North Wyke farm, made under this project, were in the range 19 to 9,720 cfu 100 ml-1 with a geo-metric mean value of 290 cfu 100 ml-1 (n = 18); and at Thorney Abbey farm were in the range 9 to 11,300 100 ml-1 with a geometric mean value of 96 cfu 100 ml-1 (n = 17). Lewis et al. (2005) reported comparable faecal coliform concentrations in the range 0 to 2378 cfu 100 ml-1 and an average value of 298 cfu 100 ml-1 (n = 17) in the gutters capturing roof runoff on ten dairy farms in California. Edwards et al. (2008) reported faecal coliform concentrations with an average value of 1974 cfu 100 ml-1 (n = 10) in roof runoff from 4 livestock farms in south-west Scotland. The measured faecal indicator concentrations in roof runoff are generally low but very variable, potentially reflecting a first-flush effect in the build up and wash out of roof pollutants (Fewtrell and Kay, 2007; Egodawatta et al., 2009). The direct microbial contamination of roof runoff on farms is most likely due to roosting birds, but may also occur from the dispersal of aerosols from manure handling operations on the farm. Defra project WA0804 reviewed six studies and reported pathogen dispersion up to 200 m in association with manure spreading at wind speeds of c. 2 m s-1 (ADAS, 2004). The faecal indicator loads associated with farm building roofs were calculated using an assumed uniform distribution of faecal coliform concentrations in the range 100 to 1000 cfu 100 ml-1. The depth of runoff was assumed equal to the local annual average rainfall. The building roof area was calculated using empirical data on 25 cattle and sheep farms surveyed by ADAS in the river Caldew catchment, north-west England (Sanders et al., 2004). The roof area was modelled as a function of the total numbers of adult dairy and beef cows on the farms. For farms with a total managed land area of 30 to 299 ha (excluding rough grazing), and adult stock densities of 0.37 to 2.02 cp ha-1, the total roof area was predicted as 16.3 m2 cp-1 (s.e. 2.8 m2 cp-1)


plus a fixed area of 1221.5 m2 (s.e. 419.9 m2) with an r2 of 60% (Figure 6.1). There was no significant difference between the individual area coefficients for beef and dairy cows, and no correlation with the numbers of sheep. The floor and hence roof areas for housed pigs and poultry are estimated based on Defra Welfare Codes. Defra project WU0102 summarised floor areas for laying hens and broilers in the range 0.08 to 0.16 m2 cp-1 for the purpose of estimating water usage in cleaning (ADAS, 2006; Defra, 2002). The Defra codes require an area of at least 2.25 m2 cp-1 for sows, and an area in the range 0.15 to 1.00 m2 cp-1 for weaners and rearing pigs in proportion to their weight (Defra, 2002; 2003).

y = 16.354x + 1221.5R2 = 0.6053

0

2,000

4,000

6,000

8,000

0 100 200 300 400

Number of Adult Beef and Dairy Cows

Tota

l Far

m R

oof A

rea

(m2 )

Figure 6.1 Relationship between total roof areas of farm buildings and the total numbers of adult beef and dairy cows for 26 cattle and sheep farms in the Caldew catchment, north-west England. Assuming that all roof runoff was delivered to a watercourse, we estimated that the faecal coliform load from roof runoff associated with the buildings for a single adult dairy cow was in the range 10×106 to 144×106 cfu yr-1 in an area with 720 mm annual rainfall. This is less than 0.005% of the annual indicator burden excreted by the animal, and we concluded that roof runoff would normally be inconsequential in calculating the farm indicator load. However, the roof runoff can significantly increase the volume of dirty water that must be managed, and increase the possibility that dirty water is spread to land at an inappropriate time. 6.3 Runoff from Manure Storage Facilities 6.3.1 Slurry Storage Slurry that is stored in cylindrical tanks or lagoons may leak and contaminate groundwater due to improper sealing or the development of a crack in the store wall. Generally, lagoons are naturally self-sealing due to the physical blocking of soil drainage pores and fissures with manure solids (Withers et al., 1998). Since 1991 the Control of Pollution (Silage, Slurry and Agricultural Fuel Oil) Regulations require that the base and walls of earth banked and other stores should be impermeable. This was defined by Mason (1992) as having a permeability of 10-9 m s-1 with a minimum thickness of 1 m. The risk that slurry stores present to the environment should


therefore be minimal providing that a catastrophic failure does not occur. This is supported by the results of Gooddy et al. (2001) who monitored the microbiological contamination of groundwaters with boreholes at eight sites in England on the Permo-Triassic Sandstone and Chalk aquifers and located adjacent to unlined slurry stores. Although Cryptosporidia and Escherichea coli. O157 were found in the cattle slurry lagoons, neither were found in the aquifer material beneath. However, care must be taken not to disrupt any store lining. Withers et al. (1998), for example, monitored the biological composition of groundwater sampled from a 76 m deep borehole situated 80 m from an unlined, earth-banked lagoon excavated in Upper Chalk and uses to store cattle slurry. Faecal coliform concentrations of up to 1800 cfu 100 ml-1 were detected in coincidence with the arrival of increased nutrient concentrations in the bottom samples. Withers et al. (1998) believed that the contamination occurred as a result of fissure flow through the unsaturated zone of the Upper Chalk following repeated disruption of the self-sealing layer during the emptying of the lagoon by dragline. Overall, however, this project has assumed zero risk of faecal indicator losses from slurry storage. 6.3.2 Solid Manure Storage Solid manure is generally stored as a stack or heap in the open. It can be stored on the farm yard, but is also often transferred to a field and stored on the field margin until spread to land. The Defra Farm Practice Survey (2006) for England reports that 44% of farm yard manure is stored in open fields with no opportunity to capture leachate. For manures stored on the hard-standing, Defra project WA0523 reported that 233 of 665 farms with livestock had concrete farm yard manure storage areas that were open to rainfall (ADAS, 1999). The manure storage areas had an average area of 373 m2. The majority drained to a tank or lagoon (58%) or were run-off to land (35%). Manure heaps stored in the open may lose significant quantities of leachate as they compact and through rainfall infiltration, and the runoff may have significant concentrations of faecal indicator organisms. Lewis et al. (2005), for example, measured average faecal coliform concentrations of 7.2×106 cfu 100 ml in yard runoff from manure storage areas (n = 18; s.e. 3.2×106 cfu 100 ml) on 10 dairy farms in California. Measurements of leachate volume and indicator concentrations from stored pig and cattle solid manures under this project reported leachate volumes equal to 9 to 83% of rainfall falling on the heap during the monitoring period (n = 8) and the proportion of the initial Escherichea coli. burden leached in the range <0.1 to 14.5% (n = 8). The geometric average proportion of the initial burden leached was 0.6%. There was no relationship with rainfall or leachate volume. For this prototype modelling framework, we have assumed that losses from open steading and field heaps can be modelled as a fixed 1% of the indicator load remaining after die-off in animal housing but before storage. Appropriate modifiers are then applied for the proportion of hard-standings connected directly to a watercourse or the proportion of fields located adjacent to streams. {Beta Parameters: Cattle: Ave 0.1 Std 0.1; Pig: Ave 0.6 Std 0.3; Poultry: Ave 0.3 Std 0.3}


6.4 Septic Tanks For completeness, the prototype modelling framework also considered the potential faecal indicator contribution from septic tanks serving the residents of a farm. There is widespread use of septic tanks for on-site treatment of waste water in houses mostly located in rural areas. The Council for Nature Conservation and the Countryside (CNCC, 2006) in Ireland, note that many tanks discharge to poorly functioning soakways or directly to field drains with an estimated 60% of septic tank discharges reaching surface waters. Only 4 of 74 existing septic tank sites in Ireland investigated by Gill et al. 2007 (reported in Holman et al., 2008) were deemed suitable for the installation of a septic tank. The most common reason for site rejection was the presence of a high water table. Calculation of the potential faecal indicator load from septic tanks required an estimate of the quantity of water discharged, and of faecal indicator concentrations in the waste stream. The average daily water consumption in England and Wales for unmetered households is 152 litres per head, based on 2001 water and sewerage company data. Kay et al. (2008) reported that faecal coliform concentrations in settled septic tank effluent were reduced by any average of 56% relative to untreated sewage. The average faecal coliform concentrations were 7.2×106 cfu 100 ml-1 (n = 42), and the average total coliform concentration was 2.5×107 cfu 100 ml-1 (n = 43). Assuming that 2.1 persons occupy the farmstead, the faecal coliform flux from the septic tank outlet was estimated to be 230,000×106 cfu day-1. This assumed that all of the effluent is discharged to surface or ground waters without further die-off or filtration. If only 1% of the daily indicator burden from a dairy cow was lost in runoff during the grazing season, and the farm was stocked at one adult dairy cow per hectare, then the total livestock burden would be c. 12,000×106 cfu day-1 for a 100 ha farm (see Section 2). The farmstead septic tank burden may therefore considerably exceed the livestock burden, even allowing for additional filtration in the leachate field. The significance of the septic tank burden depends critically on the relative indicator concentrations in human and livestock waste, and the maintenance of the system. Further work is therefore necessary to quantify this indicator source. This might be based on the work of Collick et al. (2006) who have developed a simple mass balance model that is capable of quantifying the frequency of hydraulic failure of septic tank systems. Beal et al. (2005) have also combined modelling and monitoring to demonstrate that the development of the biomat is most significant in affecting soil permeability and the risk of exfiltration of excess effluent. The limiting factor in the long term performance of the absorption system is the low permeability biomat zone that develops along the bottom and lower sidewalls of the trench that received the outflow from a septic tank. The measured saturated hydraulic conductivity of the biomat was in the range 0.15 to 0.22 cm day-1 in comparison to initial soil values of 23 to 2470 cm day-1. Infiltration rates through the biomat zone decreased by a factor of 100 to 1000 within a year.


7. Methodology for Quantifying Diffuse Sources Losses This section presents the prototype model calculations for the loss of faecal indicator organisms in runoff from field areas, associated with deposition of excreta at grazing and manure spreading. The diffuse loss calculations also include estimates of the impact of direct deposition of excreta by grazing animals into streams during livestock movement. These direct inputs to streams are unaffected by die-off and potentially represent a large contribution to the farm faecal indicator budget. 7.1 Losses by Direct Deposition The direct deposition of excreta and faecal indicator organisms into streams can occur when animals ford streams during livestock movements, and when animals enter streams for water and cooling. 7.1.1 Stream Crossing The direct faecal indicator input to streams depends on the frequency of stream crossing during stock movement, and the probability that an animal will defecate whilst crossing the stream. Aitken et al. (2001) reported that 13% of dairy farms, 19% of beef and 24% of sheep farms in the river Irvine catchment (a survey of 101 farms), and 60% of dairy farms, 10% of beef and 13% of sheep farms in the river Girvan catchment (a survey of 20 farms) had regular stock movement through watercourses. The average numbers of crossings made daily were 1.5 and 1.7 for the Irvine and Girvan catchments in Scotland. Anthony et al. (2010) carried out a survey of 600 farms in Wales. Livestock were reported as regularly walking through streams during stock movements on 18% of dairy farms, 20% of upland cattle and sheep farms; and 12% of lowland cattle and sheep farms. Based on these survey data, we estimated that 10% of farms required animals to ford streams when moving between grazing areas or travelling to the milking parlour. The frequency of stock movement was weekly for beef cattle and sheep, and four times a day (two journeys to and from the milking parlour) for adult dairy cattle. There are very few observations of the probability that animals will defecate when crossing or loafing in streams that are reported in the literature. Davies-Colley et al. (2004) monitored the impact of a herd of 246 dairy cows crossing a stream ford in New Zealand. A total of 25 defecation events were recorded when 170 cows were videoed crossing the 17 m ford, and 11 events following the passing of all 246 dairy cows along the 200 m raceway. Defecation counts on the raceway and in the ford indicated that the cows defecated 50 times more per unit length of their path through the stream than elsewhere on the raceway, but they were also travelling 10 times slower. Overall, however, the statistics indicated that only 15% of cows defecated when crossing the ford once. Demal (1982) reported on the monitoring of livestock activities at or near a stream at five cattle access sites on the river Avon, Ontario in 1982. The sites were monitored for two dry-weather days during the period from July to September when cattle were in pasture. A total of 10 access events were monitored, lasting from 1 to 45 minutes. Measurements taken during the events included the number of cattle crossing the river channel (less than 25 m wide), the number of cattle watering at the channel edge, and the number of in-stream defecations and urination. The length of stream accessible to cattle ranged from 9 to


420 m. On average during each channel access event, 76% of the animals present entered the stream, and of those 12% urinated and 18% defecated. Based on these observations, we have assumed that 10% of animals crossing streams defecate. Dairy cattle would be milked twice a day. If each cow defecates an average of 12 times a day (North Wyke Research, 1999), then 5% of the daily excreta produced during the grazing season would be direct to the stream on a farm where stream crossing was necessary. The proportion of the total faecal indicator burden deposited whilst crossing streams will be much less at landscape scale as not all farms require stream crossing. 7.1.2 Direct Access for Watering Cattle are attracted to streams and channel banks for drinking, access to palatable vegetation and shade (Kauffman and Krueger, 1984; McKergow et al., 2003). Collins (2004) reported that cattle are also attracted to small, shallow wetlands for grazing in both summer and winter. In the absence of fencing therefore, we would expect cattle to spend a proportion of the grazing day in the vicinity of running water, and directly defecating into the streams. Bagshaw (2002), for example, recorded the location and timing of beef cattle defecation in hill country in New Zealand, with either unrestricted access to water or fencing that permitted only drinking. The number of defecations was in proportion to the time spent in an area. The cattle spent an average of 4% of their day in the riparian zone (in the water or on the stream bank, and half of the defecations were in the water). The spatial pattern of defecation was unaffected by season or the presence of a water-trough. A similar study of dairy cattle estimated that 0.5% of excreta was directly deposited in the water (Collins et al., 2007). A study in Iowa of cattle with unrestricted access (Haan et al., 2007) also reported cattle defecating up to 5% of the time in the watercourse, regardless of whether alternate water sources were available within a field. Even if faeces were not deposited directly in a stream, deposition in the riparian zone may also promote the survival of bacteria due to the relatively high soil moisture content. Demal (1982) reports that pastured cattle water at a rate of 1 to 4 times per day, and that this was not affected by the breed or type of cattle. With an average access event duration of 15 minutes (range 1 to 45 minutes) and an active 16 hour day, this would indicate that the proportion of time spent in stream is in the range 2 to 6%. However, the frequency of accessing streams can also be influenced by temperature, and the provision of alternative sources of water. Franklin et al. (2009) reported that, when the temperature and humidity index indicated stressful conditions, provision of cattle with water troughs outside of riparian areas tended to decrease the time cattle spent in these areas. Hann and Russell (2008) investigated the impact of unrestricted stream access, restricted access (to a 16 foot wide crossing) and rotational stocking (with one of 5 paddocks in the riparian area) on the time spent by beef cattle in a pasture stream and in the riparian area. Time spent within the stream did not differ between the restricted access and rotational stocking management. At ambient temperatures above 80 oF, the percentage of time the unrestricted cattle spent within 110 feet of the stream increased from less than 20% to 100% at temperatures of 95 oF. This temperature effect was not observed for the cattle with restricted access. Cattle with unrestricted access spent a maximum of 2.4% of their time in the stream in any month, and an average of 1.2% in the months of May to September. Cattle with


restricted access or rotational stocking spent an average of less than 0.2% of their time in the stream. McGechen et al. (2008), in their model of river water pollution, assumed that cattle defecated directly into streams only on dry days, and more so when temperatures exceeded 17 oC. Based on these reports, we have assumed that grazing cattle will spend between 1 and 5% of the grazing day in a watercourse, providing that it is not fenced off. We have also assumed that sheep will not loaf in streams. The probability of direct access to streams, has been estimated from surveys of fencing and provision of water. Sanders et al. (2004), for example, reported that of 851 fields on 25 farms in the upland catchment of the river Caldew, that 49% of grass fields were adjacent to streams, and 25% gave unrestricted access for grazing livestock to the water. Aitken et al. (2001) reported that the percentage of farmers who used a ditch, burn, river or pond as a watering system for livestock, and therefore required free access, was 25% for dairy farms, 41% for beef and 45% for sheep farms in the river Irvine catchment (a survey of 101 farms). The percentage of farmers was 29% for dairy farms, 25% for beef and 29% for sheep farms in the Girvan catchment (a survey of 20 farms). More recently, Anthony et al. (2010) carried out a survey of 600 farms in Wales. Livestock were reported as having direct access to streams for drinking water on 53% of dairy farms, 81% of upland cattle and sheep farms; and 63% of lowland cattle and sheep farms. Analyses of data from the Countryside Survey (Ditches, Streams and Linear Features, 1998) suggest that less than 10% of field boundary length comprise watercourses with unrestricted access, so the farm survey data may be an over-estimate of the proportion of grazed fields that provide direct access. Based on these survey data, we have assumed that an average of 10% of grazed fields have unfenced direct access to streams on dairy farms, and 25% on beef and sheep farms. It is further assumed that the quantity of direct excreta deposition is in proportion to the time spent in the stream. As a consequence, we estimate that up to 1% of the total faecal indicator burden from cattle will be directly deposited in streams whilst watering or loafing during the grazing season. 7.2 Runoff from Farm Tracks Farm tracks are potentially an important source of faecal indicator organisms as runoff is enhanced from the compacted surface, and the tracks can provide direct connectivity to field gates and bridges over watercourses. In this modelling framework, we have assumed that dairy cattle frequently use farm tracks, when moving to and from the parlour. The time spent on the tracks is in the range 20 to 80 minutes per day (see Section 3.1.1). If excretion were proportional to the time spent at a location, then up to 5% of the daily indicator burden would be deposited on the farm tracks. Based on the data reported by Davies-Colley et al. (2004), the observed rate of defecation was 0.05 events per cow per 200 m travelling along a raceway (see Section 8.1.1). Assuming a walking speed of 3 km hr-1, these data would imply that a comparable 8% of the daily indicator burden would be deposited on the farm tracks. The modelling framework therefore assumes that defecation on the tracks is in proportion to the time spent on them. The probability that a farm track is directly connected to a watercourse cannot be any greater than percentage of livestock moving through watercourses on a regular basis (assumed to be 10%, see Section 8.1.1). The die-off and wash-off of faecal indicator


organisms from the tracks was then modelled using the hard-standings runoff sub-model (Section 6.1), but assuming that the track is not cleaned. The model was run for a range of faecal indicator half-lives (2 to 5 days) and rainfall washout coefficients (0.002 to 0.008 mm-1) to estimate annual losses of between 1.1 and 9.2% of the deposited faecal indicators for a site with annual rainfall of 720 mm, and between 2.5 and 18.1% for a site with annual rainfall of 1200 mm (Table 7.1). Table 7.1 Modelled range of the percentage of total faecal indicator burden lost in surface runoff from tracks that are not cleaned. The reference faecal coliform half life was varied from 2 to 5 days, and the wash out parameter was varied from 0.002 to 0.008 mm-1.

Monthly Rainfall (mm) Percentage Loss (%) 40 50 60 70 80 90 100

Minimum 0.54 0.85 1.14 1.52 1.83 2.16 2.53 Maximum 4.72 6.81 9.23 11.54 14.16 15.94 18.12

If a herd of 100 dairy cows passed along a track each day, then based on the defecation rate reported by Davies-Colley et al. (2004) we can estimate that they would deposit c. 5,000×106 cfu per day over a 100 m2 track area (length of 50 m). By application of the hard-standing runoff model, the faecal coliform concentration in track runoff was then estimated to be in the range 1×106 to 10×106 cfu ml-1 for a location with a monthly rainfall of 60 mm. In comparison, measurements were made under this project of Escherichea coli. concentrations in runoff from a dairy track at Friars Hayes farm. Measurements were made on six occasions, from July to September. The track was irrigated to simulate rainfall. The Escherichea coli. concentrations were in the range 2×106 to 1×108 cfu 100 ml-1, with a geometric average of 1.2×107 cfu ml-1 (n = 84), for runoff in the range 20 to 25 mm on each occasion. 7.3 Field Runoff and Drainage Surface runoff and drain flow are the main mechanisms for the mobilisation and delivery of faecal indicator organisms in excreta and manures deposited to fields. We utilised a conceptual model originally developed to represent pesticide losses for this part of the modelling framework. There were no accessible datasets for model verification, so the range of model outputs was compared against the few literature reports of the proportion of the indicator burden lost in drainage. 7.3.1 Literature Measurements of Faecal Coliform Losses Reported measurements of faecal indicator concentrations in field drainage are variable, reflecting a range of experimental and environment conditions. Deeks et al. (2005), for example, measured indicator concentrations in surface runoff and subsurface drainage in free draining and slowly permeable soils at three sites in England: Caerhays, Sandsend and Rowden. Surface faecal coliform concentrations were generally significantly greater than subsurface concentrations. Measured concentrations at a field site were extremely variable, with a coefficient of variation in the range 80 to 642%. Mean concentrations in surface runoff were in the range 345 to 1,031,346 cfu 100 ml-1 and in subsurface flow were in the range 232 to 5737 cfu


100 ml-1. Oliver et al. (2005) reported peak concentrations of faecal coliform in runoff and drain flow from grassland after grazing of the order 1×105 cfu 100 ml-1. Merrilees (2004) reported that drainage water from a dairy cattle grazing field contained 105 to 106 cfu 100 ml-1 during high flow conditions. Relatively few studies have reported both flow and indicator concentrations, and calculated the mass balance from which it is possible to estimate the proportion of the indicator burden that is lost. These studies are, however, critical to placing sensible bounds on the acceptable output from a loss model. Vinten et al. (2002) reported drain flow concentrations of up to 106 cfu 100 ml-1 following application of slurry to drained silty clay loam, clay loam and loamy sand soils in Scotland. The cumulative transport of faecal coliform to drains from experimental arable and grass plots ranged from less than 0.1% to 19.6% of the applied load over a period of 60 to 70 days. Losses in surface runoff were comparatively small. Vinten et al. (2004) also reported drain flow concentrations of 1100 cfu 100 ml-1 from plots grazed with sheep during June and July, or 0.4% of the estimated burden over the grazing period. Concentrations on the same site treated with slurry were only 500 cfu 100 ml-1, or 0.03% of the estimated burden. Sheep grazing losses during cooler and wetter conditions in October and November were equal to 8.2% of the estimated burden. Comparison of experimental and modelling results for slurry and faeces in different seasons led McGechen and Vinten (2003; 2004) to conclude that soil moisture conditions were critical in controlling the loss of bacteria in drain flow. When the soil was dry in the summer months, excess rainfall that entered macropores at the soil surface would enter soil micropores at depth rather than exit via the soil drains. McGechan and Vinten (2004) and Vinten et al. (2004) summarised the results of the MACRO model calibrated to represent the loss of Escherichea coli. from the Scotland experimental work, by a regression equation. Losses were predicted in the range 0.1 to 1.5% of the faecal indicator organisms present for drain flow in the range 5 to 30 mm. Duchemin and Hogue (2008) in an evaluation of the effectiveness of buffer strips, measured Escherichea coli. loads in surface runoff and soil drainage from control experimental plots receiving swine slurry on two occasions over a crop year that were less than 1% of the indicator load applied. Sinton et al. (2007) measured the leaching loss of Escherichea coli. from pats placed over drainage funnels outdoors, expressed as a percentage of the estimated coliform surviving in the pats. Monthly losses were in the range <1 to 58% for monthly drainage values in the range <1 to 84 mm. Losses were weakly correlated with drainage volume, averaging 7% of the available bacteria in each month (s.e. 1.6%; n = 20) for an average monthly drainage of 20 mm (s.e. 5.0 mm; n = 20). The majority of literature data are for experimental boxes or micro-plots with artificial rainfall, and intended to measure bacteria losses in surface runoff only. These experiments do not allow for the die-off of indicator bacteria between application and the first rainfall event that causes runoff. Thurston-Enriquez et al. (2005), for example, simulated three consecutive rainfall events producing 35 mm and separated by 24 hrs (to give a total rainfall of 104 mm) on plots treated with fresh and aged cattle manure and swine slurry. The estimated release of Escherichea coli. as a percentage of the available over the course of the three rainfall events was in the range 2.6 to 6.0% for the aged cattle manure and 7.0 to 12.4% for swine slurry. The rainfall intensity of 70 mm hr-1 represented an extreme event, with total runoff in the range 54 to 83 mm.


Stout et al. (2005) measured faecal coliforms in runoff from grassed experimental boxes that received broadcast dairy manure and artificial rainfall of 26 mm hr-1 for one hour. The release of faecal coliforms in runoff was in the range 26.6 to 45% of the available bacteria (n = 6). Soupir et al. (2003) measured faecal coliform release from cow pats under artificial rainfall of 44 mm hr-1 over one hour. The indicator loss as a percentage of the available bacteria was in the range 0.3 to 0.6% for dairy manure, and up to 14.5% for turkey manure. Using data reported in Muirhead et al. (2006) measuring the loss of Escherichea coli. in runoff from artificial cow pats experiencing rainfall of 25 mm hr-1 for 30 minutes, we calculated that the indicator loss was in the range 0.2 to 0.8% of the available bacteria in the cow pats. Although limited, and allowing for the die-off between application and the first rainfall event, these literature data indicated that average seasonal indicator losses should generally be in the range 1 to 10% of the faecal indicator burden applied to land, for rapid soil drainage of 10 to 100 mm. 7.3.2 Modelling Approach For this aspect of the modelling framework, faecal indicator bacteria losses from spread manures and excreta at grazing were calculated using an adapted pesticide leaching model that simulated the decomposition and mobilisation of chemicals with analogous properties. Losses from manures and excreta were both calculated using the same conceptual model derived from the pesticide risk leaching model. This work ignored potential losses of indicator bacteria attached to eroded soil, or losses via the groundwater pathway, as both are believed to be small compared to the loss of bacteria suspended in surface runoff and drain flow. The SWAT model (Brown and Hollis, 1986) is a simple conceptual model of the risk of pesticide loss as solute in rapid soil drainage (surface runoff and preferential drain flow). The published model was enhanced to calculate the proportion of the applied dose that is lost in surface runoff and preferential drain flow during a season rather than a single event. The model was also linked to a synthetic weather generator and used to calculate average indicator losses over a 100-year period, assuming an equal probability of manure or excreta application on each day of the year. The model was then applied to a range of climate and soil conditions, to generate a series of look-up tables summarising the model results for representative locations in England and Wales, and the results averaged for applications to land made in the winter and summer seasons. The SWAT model calculates the survival and movement of coliform (analogous to the chemical decomposition and movement of a pesticide) away from a surface zone that interacts with rainfall. On each day following application, coliform survival is modelled using first-order kinetics and they move down into the soil profile at a rate related to the soil saturated hydraulic conductivity. On any day when rainfall exceeds a critical threshold determined by soil type, the excess rainfall becomes surface runoff and preferential flow and entrains a proportion of the indicator bacteria not attached to sediment that remains in the top 2 mm of the soil surface. The empirical stream response model in the SWAT tool is based on the short-term increase in stream flow that occurs over the first 24 hours after a storm event (Brown and Hollis, 1996). It is described as moving rapidly to the streams, either as surface flow or via the soil fissure and macro-pore system and field drains if present. There is no explicit separation of surface runoff and drain flow.


The minimum standard rainfall volume required to initiate stream response are related to the Standard Percentage Runoff (SPR) and Base Flow Index (BFI) coefficients that have been derived for each soil series in the United Kingdom (Boorman et al., 1995). As catchment scale hydrological indices they integrate the generation of rapid stream flow from both infiltration excess and saturation excess processes, and there is no explicit separation of the two. This is of significance for the evaluation of mitigation options. Areas of saturation excess runoff tend to be located immediately adjacent to watercourses, whilst infiltration excess runoff or sub-surface preferential flow may occur more widely throughout a catchment. If the majority of the stream response were generated by saturation excess, then exclusion of livestock from riparian areas would be expected to have a large impact on faecal indicator losses. If runoff or preferential flow were generated more widely across a field or catchment, then such targeted options would be considerably less effective. Correct identification of the primary response mechanism is therefore critical to the selection and location of mitigation, and in the selection of an appropriate model to aid this process (Lyon et al., 2006). Conceptual models of catchment response can over-simplify the response mechanism, and it is therefore best to develop a model based on observation. For example, detailed monitoring of three adjacent pasture hill slopes in the Ozark Highlands, indicated that both infiltration excess and saturation excess runoff mechanisms occurred to varying extents (0 to 56% of infiltration excess and 0 to 26% for saturation excess) during five rainfall events (Leh et al., 2008). This is discussed further in the section on mitigation options (Section 9). The minimum standard rainfall volume represents field capacity conditions, and is modified by an antecedent Catchment Wetness Index (CWI) as used in The Flood Studies Report (Boorman, 1985). An empirical relationship relates the average CWI during the summer months to annual average rainfall (Packman, 1989). The compaction or poaching of soils would be expected to result in increased risk of runoff and therefore indicator loss. Landscape scale analyses and expert judgement have suggested that the Standard Percentage Runoff coefficient may be increased by 10 to 15% (Packman et al., 2004). Deeks et al. (2008) report on the generation of a grassland soil compaction risk map that combines soil vulnerability to compaction (based on the trafficability model of Harrod, 1979) with stocking rate. At a landscape scale, increases in the Standard Percentage Runoff of up to 6% were calculated for intensive grazing regions of the south west of England and Shropshire. The effect of soil compaction on indicator losses is also discussed further in the section on mitigation options (Section 9). Application of the enhanced SWAT meta-model required as input information on annual average rainfall and soil type, the reference half-life of the indicator bacteria (in the range 2 to 5 days at 20 oC; Section 5) and an adsorption coefficient that partitioned the bacteria between a population attached to immobile soil particles and a population attached to colloids or suspended as individual cells which we have assumed are mobile in rapid soil drainage. Data on rainfall and soil type were available from national environmental datasets. The feasible range of the adsorption coefficient was based on a review of literature. Guber et al. (2007) reported measurements of the faecal coliform bacteria adsorption coefficient for three loam and sandy clay loam soils in the United States. Adsorption coefficients were calculated with and without bovine manure, which was found to significantly decrease the numbers of bacteria attached to soil particles. Adsorption coefficients were in the range 9 to 513 ml g-1 without manure and 3 to 27 ml g-1 with manure. Chadwick et al. (2008) assessed the dispersal of faecal coliform by mixing a standard amount of faecal matter with rainfall. The material was taken from twenty randomised plots established on permanent grassland, over a period of 1 to 16 days.


Mean data for the number of faecal coliform recovered in rain water and in the faecal material were used to calculate the adsorption coefficient. There was evidence that the coefficient increased with the increasing dry matter content of the faecal matrix, and therefore time, and ranged from 8 ml-1 g to 12700 ml-1 g. The geometric mean value for fresh (1 to 3 days) values was 72 ml-1 g. For faecal indicator in recent additions of excreta or manure, lying on the surface of the soil, an adsorption coefficient of 10 ml g-1 is also suggested. This was determined from experiments by Thelin and Gifford (1983) in which losses of indicator in rainfall falling on natural and artificial cow pats of various ages were measured. Mean indicator concentrations in 15 mm of rainfall were 107 per 100 ml, from a pat containing an estimated 109 faecal indicator. Assuming a standard water content and bulk density for cattle slurry, the adsorption coefficient was estimated by application of the adsorption partition algorithm. For indicator bacteria that are well mixed with the soil, following incorporation for example, the adsorption coefficient can also be related to the surface area of soil particles or the percentage clay content. Reddy et al. (1981), for example, utilised data collated by Burge and Enkiri (1978), Enfield et al. (1976) and Zantua et al. (1977) relating virus adsorption to the surface area of soils, to calculate the coefficient as:

7.9625.9 −⋅= pA CK EQ 7.1 and Pachepsky et al. (2006) calculated the coefficient as:

98.16.110 pA CK ⋅= − EQ 7.2 Ling et al. (2002) note that Reddy et al. (1981) reported not the distribution coefficient but the retention coefficient, which will be higher as bacteria retention by straining is also included. Based on these equations and the literature data, a guideline sorption coefficient for fresh excreta and spread manure mixed with soil was estimated to be in the range 10 to 100 ml g-1. The surface runoff and drain flow model developed from the SWAT tool was applied to a number of representative soil series and climate conditions. The soils were the free draining sandy loam Wick series and silt loam Andover series; and the silty clay loam Bromyard series, clay loam Hanslope and clay Denchworth series that normally have artificial tile drainage installed. Tables 7.2 and 7.3 show that for the freely draining Wick soil series the percentage of the faecal indicators applied to the field that are lost in rapid soil drainage is in the range <0.1 to 1.0% during the summer months for annual rainfall in the range 600 to 1200 mm, and in the range <0.1 to 1.7% in the winter months, across the span of feasible adsorption coefficients and half-lives. This range of losses is used in the modelling tool to define a uniform probability distribution describing potential losses at a location. For the slowly permeable Denchworth soil series the indicator loss is in the range <0.1 to 5.2% during the summer months and in the range 0.4 to 6.7% during the winter months. Generally losses during the winter months are 3 to 8 times greater than during the summer months for locations with an annual rainfall of 600 to 1000 mm, and around 2 times greater at locations with an annual rainfall for 1000 to 1500 mm. These calculations do not account for any pollution prevention management practice such as a riparian buffer strip, nor any other measure of landscape connectivity. Absolute losses for a winter application increase significantly providing that the adsorption coefficient is low and the half-life is long.


Table 7.2 Modelled percentage of indicator load lost to surface water in surface runoff and preferential flow, for a spring / summer application, for representative soil series, across a span of feasible half-lives and adsorption coefficients.

Annual Rainfall (mm)Soil Kd DT50Type (ml g-1) (days) 600 720 840 960 1080 1200

Andover (31) ZCL (1.00) 10 5 0.03 0.10 0.23 0.42 0.67 1.01100 2 0.00 0.00 0.01 0.02 0.03 0

Bromyard (150) ZCL (0.51) 10 5 0.24 0.58 1.08 1.70 2.42 3.11100 2 0.03 0.07 0.13 0.22 0.31 0

Denchworth (306) C (0.17) 10 5 0.96 1.85 2.82 3.75 4.58 5.17100 2 0.06 0.11 0.18 0.24 0.30 0

Hanslope (707) C/CL (0.34) 10 5 0.48 1.02 1.75 2.58 3.35 4.10100 2 0.03 0.07 0.12 0.19 0.25 0

Wick (2225) SL (0.90) 10 5 0.04 0.11 0.25 0.47 0.72 1.05100 2 0.00 0.01 0.03 0.06 0.09 0

.04

.41

.35

.31

.13 Table 7.3 Modelled percentage of indicator load lost to surface water in surface runoff and preferential flow, for an autumn / winter application, for representative soil series, across a span of feasible half-lives and adsorption coefficients.


Andover (31) ZCL (1.00) 10 5 0.32 0.55 0.81 1.12 1.46 1.82100 2 0.01 0.03 0.04 0.06 0.07 0.09

Bromyard (150) ZCL (0.51) 10 5 1.87 2.54 3.15 3.79 4.35 4.76100 2 0.28 0.38 0.47 0.56 0.63 0.72

Denchworth (306) C (0.17) 10 5 5.27 5.71 6.06 6.30 6.55 6.69100 2 0.40 0.43 0.46 0.48 0.50 0.51

Hanslope (707) C/CL (0.34) 10 5 3.43 4.06 4.55 5.00 5.41 5.72100 2 0.28 0.34 0.39 0.43 0.47 0.50

Wick (2225) SL (0.90) 10 5 0.33 0.55 0.82 1.10 1.38 1.68100 2 0.05 0.08 0.12 0.15 0.20 0.25

Average faecal indicator concentrations in the rapid soil drainage were also estimated, assuming an input of 2×1010 cfu ha-1 per day, which is broadly equivalent to the fresh excretal output from two adult cows per hectare. Modelled concentrations in the summer months were in the range 0.1×104 to 2.6×104 cfu 100 ml-1 and decreased with increasing annual average rainfall (Table 7.3). Concentrations were lower in the winter months at locations with low annual rainfall, where the increase in rapid soil drainage is greater than the increase in faecal indicator loss (Tables 7.4 and 7.5). The estimated concentrations are similar to the range reported in the literature, but as averages do not match the reported peak concentrations of c. 106 cfu 100 ml-1. It was not possible to directly verify these loss estimates, but the modelled whole farm faecal indicator budget is compared with monitored loads at catchment scale in Section 8.


Table 7.4 Modelled average faecal coliform concentrations (104 cfu 100 ml-1) in rapid soil drainage from representative soil series, for spring / summer applications, across a span of feasible half-lives and adsorption coefficients. The concentrations are calculated assuming an excretal faecal coliform input of 2×1010 cfu 100 ml-1, which is broadly equivalent to two adult cows per hectare.


Andover (31) ZCL (1.00) 10 5 2.60 2.32 2.26 2.03 1.68 1.63100 2 0.13 0.10 0.10 0.09 0.07 0.07

Bromyard (150) ZCL (0.51) 10 5 2.47 2.22 1.96 1.75 1.51 1.34100 2 0.31 0.26 0.24 0.22 0.19 0.18

Denchworth (306) C (0.17) 10 5 2.26 1.87 1.49 1.21 0.99 0.82100 2 0.13 0.10 0.09 0.07 0.06 0.06

Hanslope (707) C/CL (0.34) 10 5 2.26 2.01 1.71 1.47 1.22 1.07100 2 0.15 0.15 0.12 0.10 0.09 0.07

Wick (2225) SL (0.90) 10 5 2.32 1.79 1.72 1.56 1.41 1.31100 2 0.24 0.21 0.19 0.19 0.18 0.16

Table 7.5 Modelled average faecal coliform concentrations (104 cfu 100 ml-1) in rapid soil drainage from representative soil series, for autumn / winter applications, across a span of feasible half-lives and adsorption coefficients. The concentrations are calculated assuming an excretal faecal coliform input of 2×1010 cfu 100 ml-1, which is broadly equivalent to two adult cows per hectare.


Andover (31) ZCL (1.00) 10 5 0.76 1.10 1.56 1.24 1.49 1.71100 2 0.03 0.06 0.07 0.06 0.07 0.09

Bromyard (150) ZCL (0.51) 10 5 0.96 1.15 1.53 1.74 1.68 1.88100 2 0.15 0.18 0.22 0.25 0.25 0.28

Denchworth (306) C (0.17) 10 5 0.79 0.79 0.96 1.12 1.12 1.28100 2 0.06 0.06 0.07 0.09 0.09 0.10

Hanslope (707) C/CL (0.34) 10 5 0.96 1.00 1.25 1.44 0.82 0.81100 2 0.07 0.09 0.10 0.12 0.07 0.07

Wick (2225) SL (0.90) 10 5 0.56 0.82 1.22 1.06 0.10 0.10100 2 0.07 0.12 0.18 0.15 0.01 0.01


8. Integrated Faecal Indicator Budget Model The aim of the project was to develop a prototype faecal indicator budget for representative animal types, which explicitly represented the various sources and source areas present on a farm. 8.1 Modelling Approach Sections 2 to 7 of this report provide estimates of the faecal indicator source strength in livestock excreta, survey data on the location and timing of indicator inputs, and describe sub-models for calculating their survival and mobilisation on the farm steading and in the fields. The survey data and sub-models were used to define probability distributions that describe, for example, the variability in farm management decisions such as the decision to manage animal waste as slurry or farm yard manure, the decision to allow unfenced fields and permit watering in streams, and the variability in modelled runoff losses due to uncertainty in microbial half-lives. The probability distributions were then programmed into a Microsoft Excel spreadsheet tool for each animal type. The spreadsheet tool uses a random number generator to sample the probability distributions that have been defined and is run for a large number (n ~ 10,000) of randomly generated farm system descriptions. For each random sample, the spreadsheet tool calculates the faecal indicator losses from each source and source area on the generated farm. The results can be analysed for an individual farm, or summarised as a weighted indicator budget across all the simulated farms. The output from the tool can be set up to sample only the variability due to farm management decisions, or the uncertainty in estimating the faecal indicator burden, survival and mobilisation. The spreadsheet tool is a prototype and is not fully automated. It requires the user to enter the parameters of the probability distributions manually. Figures 8.1 to 8.3 show the input data sheets for a single animal type. The input data comprise three sections that are common to all animal or farm types. 8.1.1 Farm Properties The farm properties section captures information about the structure of a farm, such as the proportion of all farms with open gathering yards and with yards directly connected to watercourses. In the majority of cases, the management decisions are binary, as signified by the green shaded cells (Figure 8.1). For example, a farm can either have or not have an open manure storage area. In this case, the tool is designed so that the randomly generated farms will either have / not have the source area, but the overall average proportion of farms with the source area will be equal to the survey average entered into the pink shaded cells (Figure 8.1). Otherwise, the model input data are sampled from the listed minimum and maximum ranges of the parameter, as signified by the orange shaded cells (Figure 8.1). For example, the faecal indicator concentration in roof runoff is shown to be sampled from the range 100 to 1000 cfu 100 ml-1. In this case, the values are sampled from a uniform probability distribution. It is possible to fix the value of these model input data by entering a number in the cells coloured purple. In the example shown, the volume of leachate from a septic tank has been fixed at the value of 315 litres day-1 and will be used for all randomly generated farms instead of sampling from the range of 210 to 420 litres day-1.


In the example shown for an adult dairy cow the frequency of stock movement has been fixed at 0.25, so that the animal makes four trips every day to the milking parlour. The proportion of manure managed as slurry has been set to 65%. In this case, it will be assumed that either all or none of the animal waste is managed as slurry on each randomly generated farm. The proportion of farms with unfenced fields adjacent to streams has been set to 10%, so we assume that 90% of farms have no unfenced fields next to streams. All of these values have been based on the survey data reviewed in Sections 2 to 5 of this report.

Figure 8.1 Prototype model spreadsheet interface showing the farm properties input data. 8.1.2 Livestock Properties The livestock properties section captures information about the daily faecal indicator load in excreta and the proportion of the year spent on the grazing and housing regime. The model input data are sampled from probability distributions in exactly the same way as the farm properties. For example, the daily quantity of faeces produced by the example dairy cow can be sampled from the range 28 to 40 kg, but is presently fixed at a value of 34 kg (Figure 8.2.). When carrying out simulations for individual animal types there is no benefit from sampling the uncertainty range for faeces output and faecal indicator concentrations as the source apportionment will be insensitive to the changing values. Therefore, these values are normally fixed. A mass balance must be maintained for the livestock calendar. Therefore, some cells are automatically calculated (signified by a light blue shading) so that, for example, the proportions of the animal year spent on the grazing and housing regime always sum to one (Figure 8.2).


Figure 8.2 Prototype model spreadsheet interface showing the livestock properties input data. 8.1.3 Indicator Loss Coefficients The indicator loss coefficients section defines the range of the proportions of indicator organisms surviving storage and the feasible losses in runoff from the different farm source areas, based on the previously described sub-models. The coefficients are specific to the soil type and climate. The coefficients shown are for a silty clay loam soil at a location with an annual rainfall of 720 mm (Figure 8.3). The proportions surviving housing and storage are sampled from a Beta probability distribution (see Section 4) whilst the runoff losses are sampled from a uniform distribution (see Sections 6 and 7).

Figure 8.3 Prototype model spreadsheet interface showing the indicator loss coefficient input data. The loss coefficients are derived from relatively simple conceptual models, and simple monte-carlo sampling of combined probability distributions, such as for the duration of manure storage and the faecal indicator die-off rate. Development of the prototype model could focus on an improved representation of soil hydrology and rapid flow generation, with explicit separation of surface runoff and sub-surface preferential flow, and a stratification methodology for sampling the loss distributions and randomly generated farm systems. 8.1.4 Model Output The model calculates the annual average faecal indicator budget for the randomly generated farm. The budget is reported for each source area as a total number of


faecal indicator organisms that can be compared with the indicator burden in fresh excreta (Figure 8.4).

Figure 8.4 Prototype model spreadsheet interface showing the model output data for a single randomly generated farm. 8.2 Source Apportionment Results The prototype model was set up for a number of the representative livestock types, using the survey and model data from the previous sections:

• Dairy Cow • Dairy Follower • Beef Cow • Sheep • Broiler • Fattening Pig

For each animal type, the spreadsheet tool was used to simulate losses for 10,000 randomly generated farm systems and the results summarised in the form of spider graphs showing the average percentage contribution for each source area (Figure 8.5). These results were generated for the silty clay loam (Bromyard series) soil at a location with an annual rainfall of 720 mm. Table 8.1 summarises the source apportionment for each animal type. Table 8.1 Modelled weighted percentage source apportionment for individual animal types using randomly generated farm systems data representative of England and Wales. Animal Type Track Fording Paddling Hard

StandingVoiding Manure

Spreading Roof Storage

Dairy Cow 2 31 22 3 22 20 0 0 Dairy Follower 0 2 33 1 40 24 0 0 Beef Cow 0 1 59 4 28 5 0 1 Sheep 0 1 0 0 98 1 0 0 Broiler 0 0 0 0 0 76 0 23 Fattening Pig 0 0 0 0 0 67 0 33 The spider graphs show both a simple and a weighted average contribution from each source area. The simple average represents the typical relative importance of the source areas on individual farms. The weighted average uses the calculated faecal indicator loss on the generated farms as the weight. It represents the importance of each source area to the total faecal indicator load from all farms in a catchment, allowing for the variation in management decisions. As the faecal


indicator loss tends to be greater from farms that permit stream access or have connected hard-standings, the weighted average shows a greater overall contribution from these source areas (Figure 8.5). a) Dairy Cow (0.55%) b) Dairy Follower (0.38%)

0

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50Track

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c) Beef Cow (0.58%) d) Sheep (0.73%)

0102030405060Track

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e) Broiler (0.11%) f) Fattening Pig (0.09%)

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010203040506070Track

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Figure 8.5 Modelled percentage source apportionment for individual animal types, using randomly generated farm systems data representative of England and Wales. The values in brackets after the animal names are the estimated percentage of the annual excreta indicator burden lost to watercourses. The results shown are for a silty clay loam (Bromyard series) soil at a location with 720 mm annual rainfall.


The percentage of the faecal indicator burden shed in excreta that is lost to watercourses varies from 0.10% for the housed livestock, through 0.55% for the adult cows, to 0.75% for adult sheep. Overall, faecal indicator losses are least from the housed livestock, reflecting the significant die-off that occurs during housing and storage. The losses from sheep are greatest due to a period of winter grazing when runoff risk is high. When integrated with the estimates of daily excreta output (Table 2.2) we estimated that an adult cow represented a similar risk to an adult sheep. This may change if the prototype model is enhanced to represent the effects of soil compaction and poaching on surface runoff generation, which is generally more extensive on dairy farms. The overall average disguises a very wide range of losses for cattle on individual farms, associated with different farm management decisions, and for sheep due to the range of the field runoff coefficient (Figure 8.6).

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Figure 8.6 Range of modelled faecal indicator losses from randomly generated farm systems, expressed as a percentage of the faecal indicator output in excreta for dairy and beef cattle, and sheep. The results shown are for a silty clay loam (Bromyard series) soil at a location with 720 mm annual rainfall. Table 8.2 Modelled weighted percentage source apportionment for the representative adult dairy animal using randomly generated farm systems data representative of England and Wales, for the Wick and Bromyard soil series at locations with 720 and 1080 mm annual average rainfall. a) Annual Rainfall 720 mm

Soil Series Track Fording Paddling Hard Standing

Voiding Manure Spreading

Roof Storage

Wick 3 45 34 5 6 6 0 0Bromyard 2 32 22 3 22 19 0 0 b) Annual Rainfall 1080 mm

Soil Series Track Fording Paddling Hard Standing

Voiding Manure Spreading

Roof Storage

Wick 3 29 21 6 28 13 0 0Bromyard 2 14 12 3 48 21 0 0


Further model simulations showed that the effect of increasing annual rainfall from 720 to 1080 mm was to increase the annual indicator loss for the adult dairy cow from 0.55 to 1.11% for the Bromyard series, and from 0.37 to 0.57% for the Wick soil series. Increasing rainfall increased the relative contributions from voiding and manure spreading, which were lower for the free draining Wick series (Table 8.2). The effect of changing the soil type or annual rainfall was small in comparison to the large uncertainty in faecal indicator concentrations in livestock excreta.


8.3 Empirical Model Verification Verification of the total modelled faecal indicator budget for any farm would require intensive monitoring of multiple sources and source areas. This was not feasible under the current project. However, it is possible to compare the model predictions with the results of catchment scale monitoring in the United Kingdom. Kay et al. (2008) have collated the most comprehensive data on measured faecal indicator concentrations and mass export from rivers in the United Kingdom. The data are taken from 15 catchment studies led by the Centre for Research into Environment and Health (CREH; University of Wales) in the period 1995 to 2005. The data were collected during the summer bathing season (15th May to 30 September in England and Wales, and 1st June to 15th September in Scotland) and broadly correspond to the modelled grazing season. A total of 205 sub-catchments were monitored, ranging in size from 0.5 to 1,130 km2, with a mix of diffuse and point sources present. The data have previously been used to establish regression models predicting indicator concentrations at times of low flow (dry weather flow) and high flow (rainfall response flow), as a function of catchment land use (Crowther et al., 2002; 2003). Kay et al. (2008; Table 3) report that catchment average geometric mean concentrations of faecal coliform increase from 1.9×103 cfu 100 ml-1 at base flow to 5.7×104 cfu 100 ml-1 at high flow for rural sub-catchments with more than 75% coverage of improved pasture (n = 15); and from 3.6×102 cfu 100 ml-1 to 8.6×103 cfu 100 ml-1 for rural sub-catchments with more than 75% coverage of rough grazing (n = 13). These values represented a significant (p < 0.001) and consistent 23 to 30 fold elevation of faecal indicator concentrations at times of high flow. Although part of this increase may reflect non-agricultural source inputs, including sewage effluent discharges and urban runoff, we can also attribute it to runoff from land with grazing animals, and entrainment of indicator organisms deposited to stream bed stores. We can assume that the times of high flow equate to the times when our modelling framework predicts rapid soil drainage. The range of measured concentrations at times of high flow for rural catchments (8.6×103 to 5.7×104 cfu 100 ml) are similar to the range of 1×103 to 2.6×104 cfu 100 ml predicted for field runoff and a stocking density of 2 adult cows per hectare (see Table 7.4). Measured concentrations also generally show a significant inverse relationship with the total volume of high flow and low flow during the bathing season, when expressed as a rainfall equivalent over the monitoring period (see, for example, Stapleton and Kay, 2006). This is supported by the modelled concentrations in field runoff for the summer months (see Section 7.2). The high flow monitoring periods are defined by a turning-point analysis of hourly river hydrographs. Chambers et al. (2005) used the data from the CREH studies to model the total volume of high flow runoff during the bathing season as a function of catchment average rainfall and reference potential evapotranspiration, and indices of the flow regime:

100)98.1525.0()295.1( 95

556.2T

BFI

HQQeQ ⋅+⋅⋅⋅

=⋅−

EQ 8.1

where


[ PETGSAARMinSAARQT ]⋅+⋅−= 475.000061.0,0.1 EQ 8.2 and QH is the total high flow runoff (mm), Q95 is the flow exceeded 95% of the time expressed as a percentage of the average flow, BFI is the catchment Base Flow Index (Boorman et al., 1995), SAAR (mm) is the long-term catchment annual average rainfall, and PETG (mm) is the catchment annual average potential evapotranspiration under grass. The hydrograph indices can be estimated from the HOST class (Hydrology of Soil Type) of the dominant soil type within a catchment. By combining the data from Kay et al. (2008) and Chambers et al. (2005) we were then able to estimate the total number of faecal indicator exported from a catchment at times of high flow during the bathing or grazing season. For a catchment with an annual average rainfall of 720 mm and the Bromyard soil series (Q95 of 10% and a BFI of 0.51) the total high flow runoff in the grazing season was in the range 10 to 15 mm. For an improved grass area, the high flow indicator export from the catchment was then estimated to be in the range 57,000×106 to 85,000×106 cfu ha-1 for the bathing season. For a rough grazing area, the high flow indicator export from the catchment was estimated to be in the range 8,000×106 to 13,000×106 cfu ha-1 for the bathing season. In each case, the additional low flow faecal indicator export would be approximately half the high flow export for the bathing season. In comparison, assuming that the improved grassland was stocked at 2 adult dairy cows per hectare, the modelling framework predicts that the annual faecal indicator loss from all source areas on a farm is 53,900×106 cfu ha-1, of which c. 80% would occur during the summer grazing season (see Figure 8.5.a). Assuming that the rough grazing area was stocked at 4 sheep per hectare, the modelling framework predicts that the annual faecal indicator loss is 65,900×106 cfu ha-1 of which c. 25% would occur during the summer grazing season (see Figure 8.5.d). Both the predicted concentrations and faecal indicator loads are reassuringly similar to those calculated from Kay et al. (2008). Any differences could be due to the uncertainty in faecal coliform concentrations in excreta (see Section 2). The model predictions of losses for the summer months also scale with rainfall, and it is possible that the data reported by Kay et al. (2008) are generally for wetter areas than simulated. The high flow concentrations in river flows measured by Kay et al. (2008) may also have been impacted by non-agricultural sources (including septic tanks and sewage effluent discharges) and the entrainment of bacteria from stores on the river bed. Given the substantial uncertainties in the individual components of the prototype model, the similarity between the modelled and measured high flow faecal indicator concentrations and budgets was a positive result.


9. Effectiveness of Mitigation Options Options for the mitigation of the faecal indicator burden from livestock excreta and managed manures have been reviewed by Oliver et al. (2009); Heinonen-Tanski et al. (2006) and Haygarth et al. (2003) and most recently by Crowther et al. (2011). Table 9.1 lists the mitigation options considered relevant to control of faecal indicators and pathogens by type of farm practice. The available options can be usefully divided into three control type categories: source control of faecal indicator shedding in excreta and survival in managed manures; mobilisation control of the timing and location of faecal indicator inputs; and delivery control by interception and treatment. The impact of the majority of options on indicator delivery to watercourses can be estimated directly from the prototype model framework. This section provides a brief review of the mitigation options and provides estimates of their effectiveness. Table 9.1 Mitigation methods for the control of faecal indicator and pathogen losses from livestock excreta and managed manures to watercourses, by type of farm practice and by type of source control. General Practice Type and Mitigation Method Control Type Housing Practice 1 2 3

Reduced stocking rate; Probiotics and antibiotics; Dietary manipulation;

Source Source Source

Steading Practice 4 5 6 7

Interception of yard runoff; Increased frequency of yard cleaning; Roofing of feeding and loafing yards; Treatment pond or wetland for yard runoff;

Delivery Source Mobilisation Delivery

Storage Practice 8 9 10 11 12 13

Increased manure storage duration; Batch storage of slurry and farmyard manures; Composting of farmyard manure; Aeration of slurry; Anaerobic digestion of slurry and farmyard manures; Acid or liming additives;

Source Source Source Source Source Source

Spreading Practice 14 15 16 17 18

Avoid spreading at times of high risk; Avoid spreading in areas of high risk; Incorporation of farmyard manure or injection of slurry; Grass filter strips; Reduce soil compaction;

Mobilisation Mobilisation Mobilisation Delivery Mobilisation

Grazing Practice 19 20 21 22 23 24 25

Avoid grazing at times of high risk; Avoid grazing in areas of high risk; Fencing of watercourses to prevent access; Riparian buffer strips; Re-routing stock movements or bridging to avoid fording; Re-routing farm tracks away from watercourses; Reduce soil compaction;

Mobilisation Mobilisation Delivery Delivery Delivery Delivery Mobilisation


9.1 Housing Mitigation Options Reduce stocking rate The simplest of housing mitigation options is a reduction in the overall stock number on a farm. It is important that this is a genuine reduction in the indicator production and not confounded by any increase in bodyweight and excreta production per animal. As well as directly reducing the number of animals shedding faecal indicator organisms, reducing the stock density may also reduce animal stress and the shedding / transmission of pathogens from the remaining stock (Oliver et al., 2009). However, the direct impact on farm productivity indicates that this would be a high cost option and unavailable to most farms unless there is some form of compensation, such as the premiums obtained for organic production or the area payments made to reduce stocking levels under agri-environment schemes. Dietary manipulation The selection of age, species, diet and management of livestock can also impact on the indicator concentrations found in excreta at source. Callaway et al. (2009), for example, report in a review of the effect of diet on coliform excretion in cattle, that populations of faecal coliforms are consistently higher in grain fed than in forage fed cattle. Fasting can also increase shedding as it decreases volatile fatty acids in the intestine that are toxic to coliform bacteria (Callaway et al.,2009; Stevens et al., 2002). Probiotics and antibiotics Probiotics are beneficial bacteria used to reduce pathogenic bacteria in the animal gut by competition or being antagonistic to the pathogenic bacteria. Probiotics are presently used extensively in the cattle and poultry sectors and can effectively reduce the shedding of specific pathogens (Callaway et al., 2004; Stevens et al., 2002). Antibiotics can have a similar effect, but the use of antibiotics for non-medicinal purposes has been illegal in the European Union (Regulation 1831/2003/EC) because of concerns that widespread use could lead to increased antibiotic resistance of pathogens responsible for human diseases. Other than the direct reduction in livestock numbers, the impact of housing mitigation options cannot be calculated. The effects of dietary manipulation or probiotics cannot be summarised in a simple guideline value for effectiveness as the rumen or gut of an animal are highly complex systems, and modifications to its function will have varying effects on different indicator bacteria and pathogens. Whether prescriptions will have a consistent impact on a range of farms also needs to be demonstrated. 9.2 Steading Mitigation Options Interception of yard runoff and increased cleaning frequency Yard cleaning and interception of yard runoff are explicitly represented in the prototype model framework. If all of the yard runoff were intercepted and routed to a dirty water store for 1 to 2 months before spreading, then we estimate that the burden can be reduced by 50 to 90% from this source due to die-off in storage. If the frequency of cleaning of yards were increased so that all were cleaned every day,


then the hard standing losses are also estimated to reduce by 25 to 30% for the representative dairy cow and 70 to 75% for the beef cow. The reduction for dairy cows is less because the majority of yards are presently cleaned daily (see Section 3). The calculation assumes that the dirty water is stored for 1 to 2 months before spreading to land. Roofing of feeding and loafing yards Roofing of the loafing and feeding yards will reduce losses from the beef and sheep on hard standing areas, scaling with the proportion of the existing open yard area that is roofed. The maximum potential reduction is 100%. For adult dairy cows, we have assumed that only the feeding and loafing yards used in winter can be covered, and not the gathering yards. Hence the maximum potential reduction is c. 40%. This potential is unlikely to be realised in many instances, as it is not possible to roof over the whole of the yard area to which animals have access. Constructed wetland or treatment pond Constructed wetlands or treatment ponds can be either surface (overland) flow or subsurface (percolation) flow systems. The subsurface flow wetland is generally a highly engineered, confined system of graded gravels and reeds, while surface flow ponds and wetlands are more similar to a natural wetland such as a marsh (Healy et al., 2007; Cuttle et al., 2007). Constructed wetlands are the most commonly used low cost, low maintenance alternatives to land spreading of agricultural waste waters in Ireland (Dunne et al., 2005; Healy et al., 2007). The effectiveness of constructed wetlands for the control of diffuse pollution from farms was reviewed by Fogg et al. (2005). Farm ponds and constructed wetlands can provide effective treatment of contaminated runoff providing that the residence time of the system is high enough to permit die-off. Sedimentation has been shown not to contribute significantly to bacteria removal as the associated particles have very small settling velocities (Boutilier et al., 2009). Retention of 90 to 99% has been reported (Kay et al., 2005; Kern et al., 2000; Gerba et al., 1999). Decamp and Warren (2000) reported average Escherichea coli. removal rates of 41 to 72% for microcosms and 96.6 to 98.9% for pilot scale systems of sub-surface flow through constructed wetlands. The pilot scale systems had a surface area of 16.8 m2. In the most recent review, Kay et al. (in press) reported the quartile range for farm pond retention as 84 to 98% (n = 25) and for constructed wetlands as 78 to 99.9% (n = 99). The median retention rate was c. 95%. Wetlands or treatment ponds are unlikely to be effective in treating the large volume of rapid runoff from field areas. The land area required to achieve an effective residence time would be prohibitive. Their application is therefore restricted to runoff from hard standings or controlled discharge from dirty water stores instead of spreading to land. Steading options to prevent or treat yard runoff can result in large reductions in the faecal indicator loss from this source area. However, the impact at the landscape scale, across multiple farms, is expected to be small as hard standing losses accounted for less than 5% of the modelled annual faecal indicator load from the representative dairy and beef cows (Tables 8.1 and 8.2).


9.3 Manure Storage Mitigation Options Increased manure storage duration The modelling framework explicitly calculates the impact of storage duration on faecal indicator die-off (see Section 4). By modifying the surveyed storage durations so that no manure was stored for less than three months, the number of faecal indicator organisms surviving storage was reduced by 50% for cattle slurry and 80% for pig slurry. The number surviving storage of solid farm yard manure was reduced by 80% for cattle manure, 20% for pig manure, and 90% for poultry litter. The impact was less for pig manure as it is already assumed that the majority of this manure type is stored for more than three months, whilst a large proportion of pig slurry and poultry litter is currently spread immediately after removal from the animal house. Batch storage of manures The modelling framework was modified to calculate the effect of batch manure storage, rather than continuous additions, on the assumption that additional storage could be provided. Adoption of batch storage of slurry was calculated to reduce the managed manure indicator load by 65 to 90% for cattle slurry, and 30 to 65 % for pig slurry. Batch storage of farmyard manure was calculated to reduce the managed manure indicator load by 95 to 99% for cattle and pig manure, and 10 to 40% for poultry litter. The impact was less for poultry litter and pig slurry due to the assumption that a significant proportion of these manure types is presently not stored at all. Composting of farmyard manure Active composting of solid manure can be an effective control of indicator bacteria and pathogens providing that temperatures increase to at least 55 oC. This requires careful management and turning of the manure pile. Turner (2002) reported that Escherichea coli. in farmyard manure, pig faeces and straw would be reduced to below detectable levels if kept at more than 55 oC for 2 or more hours. Slurry aeration Slurry aeration is carried out principally to control odour, but it can also reduce bacteria concentrations by 90 to 99.9% over a period of 5 to 30 days at low temperatures (Heinonen-Tanksi et al., 2006). Munch et al. (1987) reported average times for a 90% reduction in Escherichea coli. in aerated pig and cattle slurry of 1.4 days at 18 to 20 oC and 2.1 days at 6 to 9 oC. These compare to average times of 2.1 days at 18 to 20 oC and 9.1 days at 6 to 9 oC for non-aerated slurry. The decimation time was always shorter in aerated than in parallel non-aerated trials. Anaerobic digestion of manures Anaerobic digestion of slurry and manure involves utilising micro-organisms to break down organic material in closed reactors at temperatures of up to 75oC. The retention time in the reactor is typically 15 days at 35 oC or 10 days at 55 oC (Haygarth et al., 2005). Reductions are generally of the order 90 to 99% for mesophilic and 99.9% for thermophilic processes. Kearney et al. (1993) reported a time for 90% reduction of Escherichea coli. of 0.4 to 4.0 days for batch and semi-continuous anaerobic digestion at a temperature of 35 oC.


Acid or liming additives The addition of lime is commonly used to treat slurry following disease outbreaks. It is reported that achieving a pH of 12 for a minimum of 2 hours is sufficient to result in an effective pathogen decline (DoE, 1996). Periods of only 2 days have been found to destroy enteric micro-organisms to a level below the detection limit (Heinonen-Tanski et al., 2006). Similarly, acidification of slurry can reduce bacteria and pathogen numbers. The effectiveness of the manure storage or treatment options in reducing overall faecal indicator losses from a farm depends on the proportion of the total indicator load contributed by the spreading of managed manures. For the representative dairy and beef cows, it is in the range 5 to 24%, whilst for the broiler and fattening pig units it is in the range 67 to 76% (Table 8.1). Solid manure treatment such as composting would also reduce losses from storage on the steading. Very high (>99%) reductions are therefore possible on housed livestock systems. 9.4 Manure Spreading Mitigation Options Avoid spreading at times of high risk The model framework allows explicit calculation of the impact of changing the timing of managed manure applications to avoid spreading at times of high risk of runoff and drain flow. If all manures presently spread in the winter period were instead spread during the summer, then the losses from managed manures were estimated to reduce by 50 to 60% for all livestock types in lowland areas, due to the reduced risk of runoff and drain flow. However, manures often cannot be spread during the summer months because of the risk of contaminating forage, or because of growing crops. Solid manures applied to arable land are incorporated, which restricts application to the time between harvest and sowing of the following crop. Avoid manure spreading in areas of high risk Hydrologically sensitive areas are parts of a catchment that are prone to generating surface runoff as a result of infiltration or saturation excess. They are associated with areas of compacted soils, shallow groundwater tables and converging flows on variable topography. If the areas coincide with a pollutant source, such as recently applied manure, they are termed critical source areas. Critical source areas may occupy only a small and seasonally variable fraction of the total catchment area, but their contribution to total runoff and pollutant loss is considerably greater. More than 90% of surface runoff can be generated from only 10% of the catchment area (Gburek and Sharpley, 1998; Pionke et al., 1996; Needelman et al., 2004). A comparable ten-fold variation in surface runoff was reported by Silgram et al. (2006) for runoff plots with and without tramlines, which resulted in a four-fold variation in sediment and total phosphorus loss. The proximity of saturated runoff critical source areas to watercourses is also an important feature as the flow path length is considerably shortened and the probability of delivery increased (McDowell and Sharpley, 2002). Identifying and halting manure spreading to these areas therefore has high potential for reducing pollutant losses from agricultural land. Ulen et al. (2001), for example, reported that 4 of 15 monitored fields accounted for 74% of the total phosphorus loss within a catchment. The identification of critical source areas is the basic premise of many phosphorus loss risk indices (see, for example, Sharpley


et al., 2001). Collins and Rutherford (2004) also explicitly represented the impact of fecal deposition on seepage zones in grazing areas in their model of bacterial water quality in New Zealand. Seepage areas were areas of permanent soil saturation, direct connection and rapid delivery to a watercourse. Critical source areas will be most important on undrained soils without sub-surface preferential pathways. Although presently discussed in the context of manure spreading, the concept of a critical source area applies equally to losses from excreta at grazing. Excluding animals from saturated areas proximal to watercourses may significantly reduce losses from voiding. Hughes et al. (2009) used detailed watercourse and field boundary data to analyse the proximity of fields to watercourses. Nationally 74% of all agricultural fields were within 50 m of a watercourse. This ranged from 44% in Chalk and Limestone to 90 to 95% in Pre-Quaternary Clay and River Floodplain landscapes. It would therefore be reasonable to assume that most fields contain at least one critical source area associated with a shallow water table in proximity to a ditch or watercourse. If we then defined a critical source area as an area where the soil is at or near field capacity for most of the year, then the critical source area has greatest impact on pollutant losses during the summer months when the remainder of the field is below field capacity and less responsive to rainfall. In low rainfall areas (less than 720 mm), the field loss model predicts the ratio of winter and summer losses to be of the order 5:1 across a broad range of soil textures (Table 7.1). If 10% of a typical field area were a critical source area, then approximately 30% of the summer loss would be from the critical source area. For high rainfall areas (more than 1000 mm) the critical source area contribution would be approximately 20% of the summer loss. Avoiding manure spreading or grazing in these areas of high risk would therefore potentially reduce total losses by 20 to 30% from these fields during the summer months. It is tentatively suggested that this would be true even of drained fields as the initiation of runoff and preferential flow is sensitive to soil moisture status. Manure incorporation Rapid incorporation of manures can protect the indicator bacteria from runoff. Slurry applications can also seal soil pore spaces, thereby increasing surface runoff volume in the first storm post application (Ross et al., 1979; Withers et al., 2000; Smith et al., 2001; Tabbara, 2003). Incorporation would re-establish the soil structure. However, it can also promote extended survival as the bacteria are protected from radiation. This can increase the period of risk. Hutchison et al. (2004), for example, reported that rapid incorporation of manures contributed to prolonged survival of pathogens in soil. Quinton et al. (2003) reported higher faecal coliform concentrations in runoff from surface applied slurry compared with incorporated slurry on a sandy loam soil packed into experimental flumes. Runoff concentrations were an order of magnitude lower from the incorporated trials. However, Meals and Braun (2006) reported that manure incorporation did not result in a statistically significant reduction in faecal coliforms in runoff. Relevant data exists on the impact of incorporation on losses of phosphorus. Withers and Bailey (2003) compared the transfer of phosphorus in overland flow from forage maize plots receiving nil, surface applied and incorporated manure. Incorporation of slurry by ploughing or tine cultivation over a two-year monitoring period reduced total phosphorus export by 60% compared to a single surface application equivalent to 30 kg P ha-1. Daverede et al. (2004) demonstrated that injection reduced the respective phosphorus load by 99% and 94% for soluble phosphorus and total phosphorus, compared to surface applications. Alternatively, Grande et al., (2005) reported that


surface manure application increased total phosphorus concentrations by 79 to 125% and that incorporation, combined with a high cutting height as a residue management technique, significantly reduced phosphorus losses. Grass filter strips Grass filter strips placed at the field edge have a variable effectiveness against faecal indicator bacteria, affected by slope, flow rate, width, the proportion of bacteria that are sediment attached, and vegetation type. Coyne et al. (1998) reported a 43 to 74% reduction in faecal coliforms exported across 9 m wide grass buffer strips with a slope of 9o, for a rainfall event of 64 mm hr-1 for 90 minutes. Collins et al. (2004) reported the retention of faecal coliforms in 5 m wide grass buffer strips with a slope of 8o. Retention was greater than 95% at low flow rates of 1 litre per minute, but declined to less than 1% at high flow rates of 8 litres per minute that was equivalent to a runoff rate of 48 mm hr-1. Tate et al. (2006) measured the effectiveness of grass strips of 0.1 to 2.1 m width on slopes of 3 to 20o. The retention of faecal coliforms for the 2.1 m width buffer ranged from 99% to 50% as runoff increased from 2 to 10 mm. The effectiveness of a filter strip will therefore clearly be affected by rainfall intensity and flow concentration in an undulating landscape. MAF (2006) summarise the effectiveness and optimal width of filter strips for faecal bacteria. For the worst-case assumption of low sediment attachment, effectiveness is in the range 80 to 85% for flat or gentle slopes, and declines to 20 to 50% for steep slopes. These estimates are for buffer widths of 5 to 30 m. In the most recent review, Kay et al. (in press) reported the quantile effectiveness of vegetated buffer strips in the range 66 to 97% with a median value of 90%. The impact of filter or buffer strips also requires assessment of the proportion of the rapid event water that is surface runoff as opposed to sub-surface preferential flow. If a soil is normally drained for agriculture, then it can be assumed that the majority of the event flow is sub-surface and unaffected by a filter strip. It is also necessary to consider whether infiltration across buffer strips increases losses in sub-surface flow. For example, intensive field monitoring by Duchemin and Hogue (2009) reported that 5 m wide grass strips at the edge of corn fields receiving pig manure in Quebec reduced runoff volume by 40% and the Escherichea coli. flux by 48%. However, both the sub-surface drainage volume and indicator flux increased as a result of the strips. The overall net effectiveness of the filter strips was therefore 15% for combined runoff and drainage, and 25% for the Escherichea coli. flux. The effectiveness of the individual manure spreading options in reducing overall faecal indicator is estimated to be in the range 50 to 90% for the fields receiving managed manure. The effect is most important at farm scale for the housed livestock farms where the majority (>70%) of the faecal indicator loss results from manure spreading (Table 8.1). 9.5 Grazing Mitigation Options Avoid grazing at times of high risk The late grazing of cattle when soils are returning to field capacity presents a risk of soil damage and the rapid loss of indicator bacteria in surface runoff and drain flow. Webb et al., (2005) used the PRAM model to estimate the number of poaching risk days in the standard (180 days) and extended autumn grazing season of an


additional 13 to 35 days dependent on climate and soil type. The percent of modelled soil poaching risk days was 7 to 30% during the standard season, but increased rapidly to 26 to 80% during the extended grazing period. McGechan (2003) similarly used the MACRO model to demonstrate that the loss of phosphorus from grazed fields increased with the extension of the end of the grazing season in autumn from 16th August to 16th December, with increasing likelihood that cattle were still grazing as soils returned to field capacity. Although focused on the potential effects of extending the standard grazing season, these studies also indicate the potential benefits of shortening the existing grazing season and avoiding times of high runoff risk, either by ending grazing early (4 to 6 weeks) or by restricting cows to daytime grazing for the final 2 to 3 months of the grazing season. This would increase the likelihood that cattle are housed when soils return to field capacity. The model framework allows explicit calculation of the impact of shortening the grazing season to avoid direct excreta inputs at times of high risk of runoff and drain flow, but increasing the quantity of managed manure and time spent on the hard standing. Shortening the grazing period by one month reduced the overall indicator loss from the representative dairy and beef cows by 5 to 10%. Fencing of watercourses to prevent access Cattle are attracted to streams and channel banks for drinking, access to palatable vegetation and shade (Kauffman and Krueger, 1984; McKergow et al., 2003). Excluding livestock from high risk areas in the landscape by fencing off river banks to prevent poaching and direct excretion into the river thereby provides a method for assisting the reduction of diffuse pollution (Environment Agency, 2001; Hilton, 2003). It is also frequently important to fence off rivers as a means of reducing trampling and poaching by livestock and the risk of bank erosion (Trimble and Mendel, 1995; Belsky et al., 1999). Vidon et al. (2008) reported a 36 fold increase in summer Escherichea coli. concentrations when cattle were given unrestricted access to a stream in the Midwest. Line et al. (2003) reported the impact of excluding stock from riparian areas and watercourses. Monitoring of a 57 ha catchment over 7 years suggested a 70% reduction in the faecal coliform export. Kay et al. (2007) reported reductions of c. 65% in summer faecal coliforms exported as a result of stream bank fencing and steading runoff containment at Brighouse Bay in Scotland. Collins and Rutherford (2004) used a spatially explicit model of grazing practices in the hill country of New Zealand to calculate reductions in export of 2 to 57% due to excluding grazing livestock from riparian areas. This assessment included the impact of direct deposition into watercourses. The modelling framework explicitly calculates direct inputs of faecal coliform in excreta by cattle loafing in streams. The fencing of pasture to prevent cattle loafing in watercourses was estimated to reduce the whole farm faecal indicator load by 20 to 60% for the representative adult dairy and beef cows (Table 8.1). However, fencing may not prove as effective if animals congregate around water troughs that are most often placed at the field edge. Concentrated excretion on compacted soil can create a critical source area for pollution unless carefully managed. Re-routing stock movements or bridging to avoid fording The effect of re-routing of livestock movements away from streams or the provision of culverts is also explicitly represented by the modelling framework. The effect is to reduce the annual indicator load by 30% for the representative adult dairy cows, and by less than 2% for sheep and beef cows (Table 8.1). The difference is due to the twice daily movement of dairy cows to the milking parlour, in comparison to the


weekly movement of beef and sheep between pastures. The provision of culverts or bridges where livestock crossings of rivers are unavoidable, also helps to prevent sediment mobilization associated with river bank poaching (Line et al., 2000) as well as direct excretion into surface waters (Sharpley and Syers, 1979). But, installing cattle bridges involves capital investment, thereby rendering them impractical for many smaller faming businesses. Moving feed and water troughs Movement of feed troughs, feeding racks and water troughs for grazing stock at intervals when the soil is wet and most easily poached will reduce the likelihood of poaching damage to the soil and also improve the distribution of excreta (Cuttle et al., 2007). High stocking densities and in particular the high stocking density at gathering points around feed and water troughs can result in the compaction and poaching of the soil (James and Alexander, 1998; Mulholland and Fullen, 1991; Jewell et al., 2007). Poaching is the penetration of the soil surface by the hooves of grazing animals, causing damage to the sward. It usually occurs briefly in spring and autumn, though some soils are susceptible during high intensity summer rainfall (Patto et al., 1978). Poaching is associated with the repeated treading of soil (Scholefield and Hall, 1985), and is therefore particularly likely at the gathering sites around troughs. The consequences are reduced infiltration and increased surface runoff and soil erosion. Although only a small area of the field is generally affected, the increase in runoff can be significant across the whole field. Heathwaite et al. (1990), for example, reported runoff volumes twelve times greater on heavily grazed grass compared to ungrazed grassland. Gifford and Hawkins (1978) reviewed the impacts of grazing on infiltration and concluded that infiltration rates were reduced by 25 to 50% on grazed compared to ungrazed pastures. Working in Ireland, Kurz et al. (2006) reported that areas readily accessed by cattle were characterised by 57 to 83% lower macroporosity, 8 to 17% higher bulk density and 27 to 50% higher resistance to penetration than areas excluding cattle. The former areas were frequently associated with higher nutrient losses (soluble phosphorus) to watercourses during and immediately after the presence of livestock (Chichester et al., 1979; McColl, 1979; Jawson et al., 1982). However, the relationship between soil compaction, runoff and grazing density is not always apparent. Carroll et al., (2004), for example, found a relationship between stocking density and compaction for experimental sites at Pwllpeiran, but there were contradictory relationships between stocking density and soil bulk density at Pwllpeiran and Snowdonia. The poaching response of soils is also difficult to predict because of the complex of soil attributes affecting soil strength, compressibility and the mode of failure (Scholefield and Hall, 1985). The regular re-positioning of feed troughs is a simple method to reduce the risk of soil compaction and poaching. It is more difficult to vary the position of water troughs. This would probably require use of a bowser or installation of a number of permanent drinking points within the field, as used on dairy farms that employ a strip-grazing system (Cuttle et al., 2007). The regular re-positioning of livestock feeder rings and water troughs can help to prevent the excessive build up of excreta on the soil surface in high risk fields connected to watercourses (Clouston, 1996; Environment Agency, 2001; Hilton, 2003). This mitigation method may, however, require the provision of hard standings for rings and troughs in particularly sensitive fields. The impact of moving feed and water troughs requires an estimate of the area of soils compacted, the increased runoff, and the preferential deposition of excreta on


the compacted soils. The method of representing compacted soil is given below under ‘reducing soil compaction’. Reducing soil compaction Trampling of the soil by stock can result in sward damage, reducing vegetation cover, and compacting the soil. This can result in reduced infiltration rates and increased phosphorus loss in surface runoff and soil erosion. The objective of this mitigation option is to disrupt the compacted soil layers by shallow spiking or subsoiling. This would also improve soil aeration and allow grass roots to penetrate deeper into the soil (Cuttle et al., 2007). The impact of livestock grazing on soil compaction and erosion is widely recognised (Evans, 1997; 1998) and is not confined to upland grassland areas. Soil compaction is beginning to be recognised as a pervasive soil condition with serious and widespread implications that include increased soil bulk density, reduced infiltration and increased soil erodibility (McHugh, 2003). Willat and Pullar (1983) reported that soil bulk density increased by 20% relative to ungrazed pasture, and soil hydraulic conductivity decreased by 75%. Gifford and Hawkins (1978) reviewed the impacts of grazing on infiltration and concluded that infiltration rates were reduced by 25 to 50% by grazing compared to ungrazed pastures. Assuming that soil compaction is a widespread problem, these changes can result in large changes in surface runoff. Heathwaite et al. (1990), for example, reported runoff volumes twelve times greater on heavily grazed grass compared to ungrazed grassland Packman et al. (2004) proposed that the impact of soil compaction on soil hydrology was represented by a modification of the Standard Percentage Runoff value associated with the soil Hydrology of Soil Type (HOST; Boorman et al., 1995) class. The rational for the changes was that soil structural degradation, in the form of topsoil and upper sub-soil compaction or seasonal ‘capping’ and sealing of soil surfaces, causes a reduction in the effective soil storage, which in turn results in increased surface runoff (Defra project BD2304). The soils that were vulnerable to structural degradation were free draining silty soils; free draining sandy loam soils; and slowly permeable, seasonally wet non-calcareous loamy and clayey soils. An adjustment to the methodology was made under Defra project BD2304 so that the full adjustment was applied only to soils at high or very high risk of poaching, and one half of the adjustment to soils at low to moderate risk of poaching, using the classification presented in Harrod (1979). By application of the model to the original and revised Base Flow Index and Standard Percentage Runoff coefficients for each soil HOST type (see Table 9.2), we calculated that event runoff (combining surface runoff and preferential sub-surface drainage) would increase by 15 to 45% in areas with an annual rainfall of 1,440 mm and increase by 20 to 75% in areas with an annual rainfall of 720 mm. These calculations assume that the whole of a field area is degraded. The present model framework represents a baseline condition under which soils are not compacted or degraded. If there were evidence that over-stocking or localised poaching had resulted in soil damage and increased risk of surface runoff, we could therefore increase the baseline losses from voiding at grazing by up to 75% in proportion to the area of the field affected. The impact of a mitigation option, such as reduced field stocking rates or the rotation of water trough locations, would then be to revert to the original runoff and loss estimate.


Table 9.2 Original and modified Hydrology of Soil Type (HOST) indices used to represent the impact of soil compaction on soil hydrology (after Boorman et al., 1995; Packman et al., 2004).

HOST Class

Analog HOST Class

HOST BFI

ModifiedBFI

HOSTSPR

Modified SPR

1 3 0.95 0.90 2 14 2 3 0.95 0.90 2 14 3 7 0.90 0.79 15 27 4 6 0.79 0.64 2 15 5 7 0.90 0.79 15 27 6 8 0.64 0.56 34 44

13 3 0.95 0.90 3 15 14 24 0.38 0.31 25 49 16 18 0.78 0.60 29 47 17 18 0.61 0.51 29 47 18 20 0.52 0.47 47 59 21 23 0.34 0.21 47 60 24 25 0.31 0.17 40 49 25 25 0.17 0.12 50 60

This mitigation option is also relevant to the loss of indicator bacteria from spreading of manures to grass and arable land. Compacted tillage soils result when the soil is worked when it is too wet to be friable and is in a plastic condition (Soane, 1975). The compaction can be reduced by the use of sub-soiling, rough ploughing or cultivation, the provision of uncropped areas along headlands for turning purposes and avoiding vehicular traffic crossings in wet periods (Environment Agency, 2001; Hilton, 2003). The effectiveness of the livestock management options in reducing overall faecal indicator depends on local factors, such as the extent of soil poaching or compaction, and whether a farm allows cattle direct access to watercourses. Preventing direct access to watercourses is estimated to reduce losses from cattle by c. 30 to 60% at landscape scale, across a sample of farms. The reductions will be much greater on the small proportion of farms that give livestock free access to watercourses (Tables 8.1 and 8.2).


10. Discussion of Uncertainty We have presented the development of a prototype faecal indicator budget model for farms by synthesis of literature data on microbial source concentrations and survival, and integration of simple conceptual models with survey data on farm management decisions in England and Wales. Integration of the faecal indicator burden with modelled estimates of annual losses gives annual average losses of 75×106 cfu for the adult dairy cow; 70×106 cfu for the adult beef cow; 45×106 cfu for adult sheep; 10×106 cfu for fattening pigs; and 1×106 cfu for poultry. Losses from immature animals, lambs and calves, are higher due to the higher concentrations of faecal indicator organisms in their excreta. When calculating losses at catchment scale using agricultural census data, accounting for the short period that the animals are actually present in a catchment would reduce this effect. However, the large variability in literature estimates of faecal indicator concentrations in excreta (Section 2), and in their survival and mobilisation coefficients (Sections 6 and 7), means that predictions of absolute losses for any one animal or farm are very uncertain. The ranges of the individual model coefficients generally span an order of magnitude. That the modelled central tendency compared well to measurement data was fortuitous, as we would expect prediction errors of a similar magnitude. The modelled annual average loss coefficient varied across the randomly generated farm types (sampling the range of survival and mobilisation coefficients, as well as farm management decisions) from 0.03 to 4.8% for the adult dairy cow; from 0.03 to 2.88% for the adult beef cow; and from 0.17 to 1.38 for the adult sheep (Section 8). Table 10.1 Range of percentage source apportionment values for individual randomly generated farm systems – for the adult dairy cow, on the silty clay loam soil at a location with 720 mm annual rainfall. Animal Type Track Fording Paddling Hard



10th Percentile 0 0 0 0 7 0 0 090th Percentile 12 49 3 11 89 68 0 2Average 5 9 7 4 46 28 0 1 Table 10.2 Range of percentage source apportionment values for individual randomly generated farm systems – for the adult beef cow, on the silty clay loam soil at a location with 720 mm annual rainfall. Animal Type Track Fording Paddling Hard



10th Percentile 0 0 0 0 8 0 0 090th Percentile 2 2 87 39 97 37 2 7Average 1 2 19 8 56 10 1 3 There should be a statistical correlation between some of the uncertain survival and mobilisation coefficients. For example, the runoff coefficient for microbial loss from spread manures in the winter and summer months, and the runoff coefficient for farm hard-standings and tracks. Representing this in the modelling framework would increase the span of the confidence interval, making losses more uncertain in the


absence of field data to calibrate the coefficients and reduce the estimates of their feasible ranges. The modelling tool can be used to sample the variability due to farm management decisions alone. For example, the modelled annual average loss coefficient for the adult dairy cow ranges from 0.46% for solid manures to 0.60% for wholly slurry based systems. The loss coefficient ranges from 0.67% to 0.52% for farms with and without hard-standings connected to watercourses, and from 1.68% to 0.43% for farms with and without direct livestock access to watercourses. Varying each of the management decisions simultaneously broadens the range of possible average loss coefficients. However, the uncertainty in the microbial survival and mobilisation coefficients is more important in affecting the overall range of model outputs. The uncertainty also impacts on the relative source apportionment. Tables 10.1 and 10.2 show the 10th and 90th percentile values of the source apportionment for the adult dairy and beef cows, based on the silty clay loam (Bromyard series) and 720 mm annual rainfall scenario. The contribution to the total annual loss from excreta at grazing varied from 7 to 89% for the dairy cow, and 8 to 97% for the beef cow. The contribution of losses from the hard-standing similarly varied in the range 0 to 11% for the dairy cow, and 0 to 39% for the beef cow. These are un-weighted results for individual randomly generated farm systems, and show that the contribution from a source area on a specific farm can be very different from the typical or average farm in a catchment. The inference is that farm managers should be encouraged to assess and control losses from all source areas, rather than following guidance prepared at catchment scale that focuses activity on a only few source areas. Intensive monitoring of microbial survival and mobilisation at multiple locations to improve the estimates of the microbial survival and mobilisation coefficients would help reduce the uncertainty in the predicted losses. However, the cost implications required for the large-scale sampling that would be required to overcome the known variability of indicator concentrations in runoff and drainage waters may prove prohibitive. We suggest that the model output is calibrated against the results of monitoring at small catchment scale, for landscapes dominated by different farm systems.


11. Conclusions We have successfully developed a prototype faecal indicator budget model for farms by a synthesis of literature data on microbial source concentrations and survival, and integration of simple conceptual models with survey data on farm management decisions in England and Wales. The budget model explicitly represents the losses from all the farm source areas and the effects of the variability in management decisions between farms of the same type. It is estimated that between 0.1 and 1% of the annual faecal indicator burden in fresh excreta is lost to watercourses in runoff from all source areas, including hard-standings and fields. However, the absolute losses are very uncertain. Losses are greatest for grazed livestock, and an adult cow represents a similar pollution risk to an adult sheep. Modelled faecal coliform concentrations in runoff and drainage were of the order 104 cfu 100 ml-1 from field areas and 106 cfu 100 ml-1 from hard-standings. The predicted faecal coliform concentrations in farmstead and field runoff, and the proportions of the total excreta indicator burden lost to watercourses, are comparable to measurements made at both farm and catchment scale. The effects of varying the environment condition were small relative to the uncertainty in source excreta faecal coliform concentrations. However, there was a large variation in modelled losses between farms of the same type, due to differences in management decisions. The relative source apportionment and the microbial loss from a typical farm management system can be very different from a weighted average derived from the results for all management system combinations. This serves to illustrate that average estimates of loss that are used with livestock census data to provide catchment and national scale estimates of pollutant losses should be derived from farm scale models that explicitly represent the range of practices. The modelled source apportionment confirms that allowing grazing cattle free access to watercourses for watering can substantially increase microbial pollution by direct deposition. Loafing in and fording of streams was estimated to account for c. 50% of the annual faecal coliform budget for adult dairy and beef cattle. The relative contributions of managed manures and excreta at grazing (c. 20%) are similar for the dairy cow on a slurry based system, but excreta at grazing is more significant for the beef cow due to the more complete die-off of bacteria during storage of solid farm yard manure. Runoff from hard-standings and tracks represented only a minor contribution (<5%) for grazed livestock, but this would increase if mitigation methods are put in place to reduce field losses. For housed livestock, runoff from hard-standings where solid manure is stored can make a large contribution (c. 25%) to the annual budget if yard runoff is not captured, but the largest contribution (c. 70%) is from the spreading of managed manure. The explicit source apportionment enabled estimation of the effects of mitigation methods targeting the different source areas on a farm. For cattle, the most effective mitigation methods were improved stock management, especially the fencing of watercourses. Increased manure storage duration would be effective for pig and poultry with solid manure systems, and the introduction of vegetative buffer strips around improved pasture and arable land would be effective against losses from both excreta at grazing and the spreading of managed manures. Each of these mitigation methods is estimated to reduce total farm losses by at least 50% for cattle and housed livestock. All farm source areas would need to be controlled simultaneously to achieve a 90% reduction in losses. However, losses from grazing sheep, especially in upland areas, cannot be so easily controlled as they are almost exclusively associated with excreta at grazing, with a large proportion of the loss


occurring during the winter months. In this case, it would be necessary to implement a strategy of avoiding grazing of high risk fields with critical source areas. 11.1 Recommendations for Further Work The prototype modelling tool was developed as a Microsoft Excel spreadsheet. The current tool is not fully automated and requires manual entry of the runoff coefficient and farm management data for each animal type. This could be automated and could be made more statistically robust by transferring into a more statistically based programming language. The approach taken to randomly sampling the farm management decisions and runoff coefficients is also simplistic, and would benefit from a stratification strategy, such as Latin Hyper Cube sampling, and the introduction of correlated random numbers. The model would benefit from further refinement and verification against the statistical models developed by Kay et al. (2008) at catchment scale. The identified software developments could then be carried out along with further technical work to estimate microbial survival during transport in river channels, to estimate faecal indicator concentrations at the point of impact, i.e. bathing waters and shellfish harvesting areas.


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Appendix A - Additional Information of Faecal Indicator Organisms This prototype modelling framework is targeted at an estimation of the faecal coliform burden from agricultural land delivered to watercourses. The concentration of faecal coliform is only an indicator of faecal pollution, and although correlated with the presence of true pathogens, is not a robust means for the source apportionment of the true pathogen burden in a catchment. Pathogen prevalence will be spatially variable and specific to the pathogen type and the pathogen and indicator bacteria survival rates will also differ. The prevalence and levels of pathogens will vary between animal type. Table A.1 summarises the results of a recent survey of pathogen prevalence and concentrations in animal manures in Britain. There is also no reason for the indicator and pathogen concentrations and survival rates in point and diffuse inputs to be the same and stable. The ratio between the numbers of pathogens and numbers of indicator organisms will be spatially and temporally variable. For example, the number of indicator organisms in point source sewage effluent will be relatively constant whilst numbers of pathogens will vary according to levels of disease in the local community (Wyer et al., 1995). Table A.1 Summary of percentage prevalence (P) and geometric mean concentration (G; cfu g-1 in positive samples only) of zoonotic agents measured in British livestock manures (Hutchison et al., 2005).

Cattle Pig Poultry Sheep Param Fresh Stored Fresh Stored Fresh Stored Fresh Stored

Escherichea coli O157

P 13.2 9.1 11.9 15.5 n.t n.t 20.8 22.2

G 1.20×103 2.60×102 3.90×103 1.30×103 n.t n.t 7.80×102 2.50×102

Salmonella spp. P 7.7 10 7.9 5.2 17.9 11.5 8.3 11.1 G 2.10×103 2.50×103 6.00×102 6.10×102 2.20×102 4.00×103 7.10×102 5.80×103

Listeria spp. P 29.8 31 19.8 19 19.4 15.4 29.2 44.4 G 1.10×103 1.10×103 3.10×103 6.10×102 8.30×102 3.30×102 2.00×102 3.00×102

Campylobacter spp.

P 12.8 9.8 13.5 10.3 19.4 7.7 20.8 11.1

G 3.20×102 5.30×102 3.10×102 1.60×103 2.60×102 5.90×102 3.90×102 1.00×102

Cryptosporidium parvum

P 5.4 2.8 13.5 5.2 n.t n.t 29.2 n.d

G 1.90×101 1.00×101 5.80×101 3.30×101 n.t n.t 1.00×101 n.d

n.t. : not tested n.d : not detected The faecal coliform are a member of the total coliform group. The coliform group is diverse and it is widely accepted that they can be considered normal inhabitants of many soil and water environments which have not been impacted by faecal pollution (Stevens et al., 2003). The total coliform are gram negative, non spore forming, oxidase negative, rod shaped facultative anaerobic bacteria that ferment lactose to acid and gas within 24 to 48 hours at 36 oC. Faecal coliform meet this basic definition but are also able to grow and ferment lactose at 44.5 oC. The faecal coliform include Escherichea coli., Kelbsiella, Enterobacter and Citrobacter. Escherichea coli. are thermotolerant coliforms that produce indole from typtophan and are considered a definitive indicator of faecal pollution as they are the only coliform that are an exclusive inhabitant of the gastrointestinal tract. The majority of faecal coliform indicator bacteria are Escherichea coli.


The revised European Bathing Water Directive (98/83/EC) specifies that intestinal enterococci concentrations are to be monitored to assess health risk (Kay et al., 2004). Enterococci are a sub-population of the faecal streptococci group, and include Enterococcus faecalis, E. faecium, E. durans and E. avium. Faecal streptococci are gram positive, catalase negative cocci from selective media that grow on bile aesculin agar and at 45 oC, belonging to both the genera Enterococcus and Streptococcus. The faecal streptococci group includes Streptococcus bovis, and S. equinus. Enterococci are all faecal streptococci that grow at pH 9.6, 10 and 45oC. An advantage over the Escherichea coli. as an indicator of faecal pollution is that the enterococci generally do not grow in the environment and have been shown to survive longer, notably in saline waters. The prototype model framework presented here could be used to assess the enterococci burden from agricultural land by replacing the source indicator concentrations and half-lives for faecal coliform with appropriate literature values. It is not appropriate to simply scale the values for faecal coliform as the available data for enterococci and faecal streptococci are very variable and do not follow simple rules relative to faecal coliform values, potentially reflecting the number of species that make up the group. Geldreich (1978) reported average faecal streptococci numbers per gram of wet weight, of 3.4×106 cfu g-1 for chickens; 1.3×106 cfu g-1 for cattle; 84.0×106 cfu g-1 for pigs; and 38.0×106 cfu g-1 for sheep. The ratio of faecal coliform to streptococci concentrations in fresh excreta was 0.18 for cattle; 0.38 for chickens; 0.04 for pigs; and 0.42 for sheep. Weaver et al. (2005) also measured faecal streptococci concentrations in fresh cattle faeces. The average faecal streptococci concentration in fresh cattle faeces from pasture was 0.11×106 cfu g-1; and in fresh sheep faeces was 0.07×106 cfu g-1. Six samples were taken for each type of livestock. All reported concentrations have been re-expressed as wet-weight. In contrast to the data of Geldreich (1978), the ratio of faecal coliform to streptococci concentrations for cattle and sheep was 5.4 and 13.3 respectively. Pourcher et al. (1991) also report varying ratios, with the majority greater than one, in contradiction to an often reported ratio of less than one for livestock manures, compared to a ratio of 4 or more for human derived sewage (Sinton et al., 1993). Sinton et al. (2007) reported Escherichea coli concentrations in fresh cow pats of 2.2×105 to 3×106 cfu g-1 compared to faecal streptococci concentrations of 6.9×105 cfu g-1 to 7.2×106 cfu g-1. Enteroccoci concentrations were much more variable, in the range 99 to 1.3×105 cfu g-1 on a dry-weight basis. Measurements of faecal enteroccoci concentrations, made during the field work element of this project, for fresh faeces from adult beef and dairy cattle were in the range 8.1×103 to 1.2×106

with a geometric mean value of 2.1×105 cfu g-1 based on a sampling of 30 cow pats. Measurements under this project for fresh faeces from sheep had a geometric mean value of 4.9×105 cfu g-1 wet-weight based on a sampling of 15 pats. The measured concentrations were both higher and lower than the measured concentrations of faecal coliforms, and the ratio was not significantly different from one. In contrast, measurements of the ratio of faecal coliform and enterococci concentrations in stored manures generally gave the expected values of less than one. The average ratio for dairy slurry samples, taken from storage after 1 week to 7 months, was 0.39 (n = 29), and for stored pig slurry was 0.39 (n = 12). However, this may be because of the greater survival of the enterococci during storage rather than a difference in source concentrations.


The reported die-off rates for faecal coliforms and enterococci show considerable overlap. Reddy et al. (1981) reviewed die off rates for Escherichea coli in the range 0.15 to 6.39 days-1 (ave 0.92, std 0.64 n 26); 0.07 to 9.10 days-1 for faecal coliforms (ave 1.53, std 4.35, n 46); and for faecal streptococci of 0.05 to 3.87 days-1 (ave 0.37, std 0.69; n 34). Sinton et al. (2007) also found that enterococci survival rates were only marginally longer than for Escherichea coli. The decimal reduction times were in the range 38 to 46 days for Escherichea coli; 29 to 38 days for faecal streptococci; and 38 to 58 days for enterococci in cow pats over four monitoring seasons. Medemar et al. (1997) also measured survival rates for enterococci in natural river water that were not significantly different than for Escherichea coli. Howell et al. (1996) reported that faecal coliform die-off rates were significantly higher than for faecal streptococci at a range of temperatures, and when the substrate was water, sand or loam. However, the apparent half-lives of faecal coliform exceeded those of faecal streptococci, even though the mortality rates were higher, because of faecal coliform growth shortly after deposition. Sherer et al. (1992) reported measured faecal streptococci die off rates that were greater than for faecal coliform in waters with and without suspended sediment. The half-lives were 11 to 30 days for faecal coliform and 9 to 17 days for faecal streptococci at ambient temperatures of 8 oC. The die off rates for different species of faecal streptococci are different, and this may explain the variability in survival rates between reported studies. When the source material is cattle, faecal streptococci die off may be faster than when it is sewage derived because of a difference in species composition. The variability between reported studies, and the complicating factor of microbial re-growth, is such that it was hard to justify allocating different source concentrations or die off rates for the faecal streptococci in the prototype modelling framework. A larger database of measurements using consistent methodologies for a range of livestock manure is required, with an assessment of the species composition of the faecal streptococci and enterococci.


Appendix B – Beta Distribution The prototype tool was developed as a Microsoft Excel spreadsheet. The tool relies on a function to sample a Beta probability distribution, characterised by a mean and standard deviation, which is not part of the default software installation. The following user defined function is used: Function BETINV(ByVal probability As Single, ByVal mean As Single, ByVal stdevn As Single, Optional lowerbound, Optional upperbound)

Dim u As Single, L As Single, m As Single, s As Single, n As Single

u = 1 L = 0

If Not IsMissing(upperbound) Then u = upperbound If Not IsMissing(lowerbound) Then L = lowerbound

m = (mean - L) / (u - L) s = stdevn / (u - L) n = m * (1 - m) / (s ^ 2) - 1

BETINV = L + (u - L) * Application.BetaInv(probability, m * n, (1 - m) * n)

End Function



This report to be cited as:

Anthony, S. G. and Morrow, K. (2011) Prototype Farm Scale Faecal Indicator Budget Model. Final report, Defra project WQ0111 – Faecal Indicator Organism Losses from Farming Systems (FIO-FARM), 89 pp.

prototype farm scale faecal indicator budget model -...

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