a simple method to identify areas of environmental risk due to manure application

A simple method to identify areas of environmentalrisk due to manure application

Héctor Flores & José Luis Arumí & Diego Rivera &

L. Octavio Lagos

Received: 30 November 2010 /Accepted: 15 July 2011 /Published online: 3 August 2011# Springer Science+Business Media B.V. 2011

Abstract The management of swine manure isbecoming an important environmental issue inChile. One option for the final disposal of manureis to use it as a biofertilizer, but this practice couldimpact the surrounding environment. To assess thepotential environmental impacts of the use ofswine manure as a biofertilizer, we propose amethod to identify zones of environmental riskthrough indices. The method considers two pro-cesses: nutrient runoff and solute leaching, anduses available information about soils, crops andmanagement practices (irrigation, fertilization, androtation). We applied the method to qualitativelyassess the environmental risk associated with theuse of swine manure as a biofertilizer in an 8,000-pig farm located in Central Chile. Results showedthat the farm has a moderate environmental risk,but some specific locations have high environmen-tal risks, especially those associated with impactson areas surrounding water resources. This infor-mation could assist the definition of better farm-level management practices, as well as the preser-vation of riparian vegetation acting as buffer strips.The main advantage of our approach is that it

combines qualitative and quantitative information,including particular situations or field featuresbased on expert knowledge. The method isflexible, simple, and can be easily extended oradapted to other processes.

Keywords Swine manure . Environmental risk . Farmmanagement

Introduction

During the last decade, the swine industry in Chilehas shown high growth rates (21.4% per year), mainlydue to the expansion of the internal market and theopening of new international markets (Peralta et al.2005; Echavarri 2009). However, these new markets,as well as the natural evolution of Chilean regula-tions, have imposed new restrictions on producers.Additionally, there is an increasing sensitivity andconcern on the part of stakeholders (citizens, con-sumers, workers and industries) about environmentalissues and public health. If wastes from agriculturalactivities, like intensive swine production, are notproperly managed, conflicts between civil society andproducers will persist in the short-term (Sperberg1996; Sepúlveda and Bastida 2005). Consequently,proper management of swine manure is becoming animportant issue in Chile for two reasons: the increase

Environ Monit Assess (2012) 184:3915–3928DOI 10.1007/s10661-011-2233-1

H. Flores : J. L. Arumí :D. Rivera (*) : L. O. LagosDepartment of Water Resources, University of Concepcion,Concepcion, Chilee-mail: [email protected]

of the number and size of confined intensive livestocksystems results in higher amount of both solid andliquid wastes, and public concern about the protectionof the environment (Peralta et al. 2005; Llona and Faz2006).

The disposal of farm soil of organic wastes (manure)generated by intensive livestock production is one of themost effective and economical methods to overcome theproblem of waste storage and disposal (Westerman andBicudo 2005; Schröder et al. 2005; Yagüe et al. 2008).From an agronomic perspective, the application ofmanure increases the capacity of the soil to retainwater, increases the amount of nutrients in the soil, andreduces the use of synthetic fertilizers (ConsejoNacional de Producción Limpia [CNPL] 2007). Onthe other hand, manure management practices are oneof the farming aspects with the greatest effect on theenvironment (Dumont 2000), requiring that the amountof manure (or slurry) applied to the soil be adjusted tocrop needs and soil characteristics.

Agriculture and livestock are considered the mainsources of non-point pollution of water resources (Alfaroand Salazar 2005; Sperberg 1996). Therefore, as notedby Behera and Panda (2006), the assessment of thepotential environmental impacts of both point and non-point pollution is the basis for the development ofeffective management strategies. Compared to expen-sive and time-consuming field experiments to measurethe effects of manure application, mathematical modelsare a fast and inexpensive approach to study optimalmanagement practices (Arumí et al. 2001b). In thisframework, the cooperative and recursive use ofsimulation models and monitoring systems is a suitabletool to define and evaluate management practices, andfor production planning (Gassman et al. 2006; Arumí etal. 2001b; Rivera et al. 2005). However, the simulationmodel as well as the monitoring system must be linkedto a conceptual model that represents and connectsrelevant processes and variables influencing the func-tioning of the system as a whole.

To assess the potential impact and drawbacks of theuse of swine manure as a biofertilizer, we propose asimple method to identify zones of environmental risk(e.g., nutrient losses due to irrigation), through indicesof vulnerability and risk. This information could beused to assist the processes of decision-making andmodeling. To validate the method, we analyzed andidentified zones of environmental risk of a typicalfarm in the Central Valley of Chile.

General background

Vulnerability and risk

All human activities have certain levels of risk. Risk isthe probability that damage or effects adverse to a systemwill occur (Gómez 2001; Pruzzo 2006) due to exposureto a hazard. Therefore, it is necessary to identify andrank the risks to determine whether the risks areacceptable or have to be reduced. Consequently, to facethe risk, it is necessary to know the hazards. A hazard isa potential source of danger, and can be a situation,agent or element. The final outcome of risk acting on asystem depends on the vulnerability of the system,which is referred to as the susceptibility of the systemto be affected (by a given risk). Moreover, differentsystems have different degrees of vulnerability to thesame hazard, and the vulnerability of a system dependson the spatiotemporal scale (Belmonte et al. 2008a;Birkmann and Nishara 2008), e.g., low vulnerability atwatershed scale could be masking high vulnerabilitysituations at field scale. Additionally, it is important toknow how risks and vulnerabilities change in the shortand long term, and how they affect the effectiveness of

Fig. 1 A flowchart for vulnerability analysis

3916 Environ Monit Assess (2012) 184:3915–3928

Fig. 2 A conceptual model for a typical farm in Central Chile

Table 1 Scores for slope: nutrients runoff (Centro de Informa-ción de Recursos Naturales [CIREN] 1999)

Slope Slope (%) Score

Plain 0–1 1

Gentle 1–3 2

Moderate 3–8 3

Steep 8–15 N.apli

Table 2 Scores for protection against nutrients runoff by soilcover (Peralta 1976; Centro de Ciencias Ambientales (EULA)2002)

Soil cover Score

Forest 1

Artificial grassland (e.g., alfalfa, ryegrass, clover) 2

Cereals 3

Vegetables 4

Grassland 4

Fallow 5

Environ Monit Assess (2012) 184:3915–3928 3917

the actions taken to reduce or eliminate the risks(Naciones Unidas 2005; Gómez 2001; Cardona 2001).

An analysis of vulnerability (Fig. 1) is not limited toquantitative approximations, but also includes qualita-tive aspects (Birkmann and Nishara 2008) and shouldbe understood as a dynamic process, affected by thecombination of anthropogenic activities and extremenatural events, and the system’s adaptation ability orresilience (Gómez 2001; Birkmann and Nishara 2008).The methodology used for an analysis of vulnerabilityhas to be adapted to the specific features of each case,like the spatiotemporal scale, as well as to differentcombinations of qualitative and quantitative informa-tion. Moreover, in most cases could be desirable todesign a case-specific methodology (Birkmann andWisner 2006; Belmonte et al. 2008a).

Methods

Constructing a risk index

In order to identify zones of environmental risk wedeveloped a method based on risk and vulnerabilityindices through the superposition of reclassifiedthematic maps (soil type and soil use, farm manage-ment, fertilization level, among others). The first step

is to build a conceptual model of the system understudy representing local characteristics, and to iden-tify and rank key variables affecting, directly orindirectly, the relevant processes. After the determi-nation of zones with environmental risk, it is possibleto tailor management practices according to each fieldto reduce the impact on the surrounding ecosystems(Belmonte et al. 2008a). Figure 2 shows a conceptualmodel for a typical farm in Central Chile that usesswine manure as a fertilizer. As shown in the figure,the sustainable management of the farm is associatedwith proper (good) fertilization and irrigation practi-ces, and the environmental risks are associated withnon-point pollution coming from the farm and reach-ing the surrounding water resources. The maintransport processes in this case are nutrient runoff andsolute leaching. The former accounts for the transport ofnon-mobile pollutants, like phosphorus (Morgan 1997;Morin and Rey 2009; Quiñonez and Dal PozzoMontuchio 2008; Espinosa 2007), while the latter isthe main transport process of mobile pollutants likeNitrogen compounds (Auge 2004; Martínez et al.1998; Arumí et al. 2001a; Monari 2004).

An important issue is to avoid the over-parameterization of the indices, because this practiceincreases the costs of application and discouragesimplementation by potential end-users. Thus, it is a

Texture Nutrients runoff Solute leachingScore

Sand, coarse sand 1 6

Sandy loam (with fines), sandy loam 2 5

Loamy sand, loamy sand (with fines) 3 4

Loamy silt, loamy, loamy sand (very fine) 4 3

Loamy clay, loamy silty clay, loamy sandy clay 5 2

Clay, silty clay, sandy clay 6 1

Table 3 Scores for soiltexture: solute leachingand nutrients runoff(Elizondo and Casanova1994; Espinosa 2007;Peralta 1976)

Table 4 Scores for soil depth: nutrients runoff (Centro deInformación de Recursos Naturales (CIREN) 1999)

Depth Depth (cm) Score

Deep >100 1

Moderate 75–100 2

Slightly deep 50–75 3

Shallow 25–50 4

Very shallow <25 N.apli

Table 5 Score for water table depth: solute leaching

Water table depth Depth (m) Score

Shallow 0–0.5 N.apli

Moderately shallow 0.51–0.8 5

Moderate 0.81–2.0 3

Deep 2.1–6 2

Very deep >6 1


good strategy to use and take advantage of available,albeit often scarce, data. In our study we usedinformation from the Centro de Información deRecursos Naturales (Center of Information of NaturalResources [CIREN] 1999), a Chilean governmentalagency that provides within-farm cartography for soiltypes, drainage classes, and soil field capacity, amongother relevant data (scale 1:20,000). This informationis readily available and could be used in conjunctionwith expert knowledge.

A risk index for swine manure application

To calculate the risk index for the use of swinemanure as biofertilizer, RIm, we intercepted the valuesfor risk associated with nutrient runoff and soluteleaching (Eq. 1) to each field or sub-field as:

RIm ¼Xn

i¼1

Li �Wi \Xn

i¼1

Lwi �Wsi ð1Þ

Irrigation method Operations Score

a. Weights for water application method

Central Pivot Application rate < Infiltration rate, Qualified workers,Scheduling

1

Scheduling 2

Basic operation 3

Sprinkler Application rate < Infiltration rate, Qualified workers,Scheduling

2

Scheduling 3

Basic operation 4

Surface Discharge control, furrow design, use of pipes 4

Basic operation 5

b. Scores for swine application method

Central Pivot Application rate < Infiltration rate, Qualified workers,Scheduling

1

Scheduling 2

Basic operation 3

Sprinkler Application rate < Infiltration rate, Qualified workers,Scheduling

2

Scheduling 3

Surface Discharge control, furrow design, use of pipes 4

Basic operation 5

Table 6 Scores forwater application methodand manure applicationmethod for solute leachingand nutrient runoff

Soil conservation practice Time Score

Manure application September–March 1

April 2

May–August 3

Not defined 3

Filter Strip Yes −1No 1

Soil nutrient budget Yes −1No 1

Application following phenological stages Yes −1No 1

Downstream creeks Yes 1

No −1

Table 7 Scores for soilconservation practices:nutrient runoff


where Wi and Wsi are the assigned scores for the rangeswhere each ith variable lays (Tables 1, 2, 3, 4, 5, 6, 7,8 and 9), and L and Lwi are the weights for each ithvariable associated with solute leaching and nutrientrun off, respectively (Tables 10 and 11). The range ofvalues obtained by applying Eq. 1 was ranked to definethe levels of environmental risk as: low, low-moderate,moderate, high-moderate, high, and extreme (Table 12).

To define the weights of each variable affecting bothsolute leaching and nutrient runoff we applied a heuristicmethod over primary information from a bibliographicreview and a panel of experts composed of scientistsfrom the schools of Agronomy and AgriculturalEngineering at the University of Concepción (Tables 10,11 and 12). Wi and Wsi were defined following a naturalprogression, ranging from 1 to 6, but considering twoexceptions. The first one is the combination of theirrigation method (Table 9a) and manure applicationmethod (Table 9b): the two values are averaged androunded up to the next positive integer. The secondexception is that when it is not recommendable, underany circumstance, to apply swine manure to the field,weight tables show “N.apli”, meaning “No applica-tion”. For the computation of RIm, the correspondingweight has a very high value (e.g., 100) as a constraint.

It is worth noting that Eq. 1 is deeply rooted infuzzy logic, which allows codifying common knowl-edge that is mostly qualitative and not necessarilyquantitative, into a mathematical language. This tool

is able to simultaneously process subjective informa-tion coming from a panel of experts and objectiveinformation (collected data) coming from the system(Medellín et al. 2004). This process of construction ofknowledge is evidenced in the weight definitions foreach variable related to nutrient runoff and soluteleaching (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10).Indeed, the method is a simple fuzzy inferencesystem, which assigns an output value to an inputvector based on a set of rules (Lee 1990). In our case,we used if–then rules of the type:

If X1 is A1ð Þ and X2 is A2ð Þ and � � � Xn is Anð Þ; then Y is Bð Þð2Þ

Case study

We applied the method to qualitatively assess theenvironmental risk associated with the use of swinemanure as biofertilizer in an 8,000-pig farm. Thefarm, Fundo San Guillermo, is located 22 km north-west of the city of Chillán (Figs. 3 and 4), and has anarea of 240 ha, with almost-plain topography in twoterraces. Indeed, the farm signed a clean productionact, which is a step ahead of Chilean regulations. It isworth noting that the organic orchards are protectedby natural barriers like the Cato River and smallcreeks, and by artificial barriers like irrigationchannels and drains. Both natural and artificialbarriers act as biofilters or buffer zones, interactingwith farm operations (Rivera et al. 2005). Raw swinemanure is processed before application beginningwith a parabolic screen, and followed by a conicsettler. The manure (98% water) is then stored duringwinter in an 8,000-m3 pond until the irrigation season.During the storage phase, the manure achieves anacceptable degree of biological stabilization (BODless than 2000 mg/l; Belmonte et al. 2008b). At the

EFC (mm) Score

>250 1

200–249 2

140–199 3

90–139 4

50–89 5

<49 6

Table 8 Scores foreffective field capacity(EFC; 1 m depth) for soluteleaching and nutrient runoff(Monari 2004)

Class Scores for nutrient runoff Scores for solute leaching

Excessive 1 N.apli

Good 2 5

Moderate 3 4

Imperfect 4 3

Poor 5 2

Very poor N.apli 1

Table 9 Scores for drainageclass for solute leaching andnutrient runoff (Centro deInformación de RecursosNaturales (CIREN) 1999)


beginning of the seeding season, the farmer calculatesa nutrient budget considering manure and fertilizer asinputs to the soil, and crop requirements as output,including in the calculation both the previous nutri-tional status of the soil and losses. Also, theapplication of manure to the field depends on properclimatic conditions (e.g., no application on rainydays).

The farm’s economy is based on three differentexploitations: (1) swine raising, which generates96 m3 of organic waste per day; (2) organic orchards,accounting for almost 70% of the area; and (3) annualcrops. The owners decided to use the organic waste asa biofertilizer, irrigating annual crops (corn, sugarbeet, wheat, oat, and chicory) with swine manure.This decision was made in order to decrease theproduction costs, but also to decrease the use ofartificial fertilizers and protect the environment.

The climate is Mediterranean, with a water deficitduring the summer (January and February) (DirecciónGeneral de Aguas 2004). The mean annual tempera-ture is 14.1°C (mean minimum temperature 7.6°C andmean maximum temperature 20.6°C), with meanannual precipitation of 1,025 mm (mean minimum is12 mm for January and mean maximum is 200 mmfor June). Soils are formed from recent volcanic ashes(postglacials) deposited over non-cemented fluvial

and fluvio-glacial materials. The predominant texturesare silt loam and clay loam (Brito 2006). The farm islocated in the hydrologic zone created by the Catoand Ñuble rivers, where a strong groundwaterinteraction between rivers exists (Dirección Generalde Aguas 2004). The main sources of recharge to thegroundwater system are infiltration from the ÑubleRiver, water losses from irrigation channels, andprecipitation (Brito 2006).

Results

For the case study, the risks associated with soluteleaching (Fig. 5a) and nutrient runoff (Fig. 5b) areboth moderate, accounting for 68% and 58% of thetotal area, respectively. A panel of experts qualita-tively validated both indices. However, as seen inboth figures, there are extensive fields (labeled A andB) where the risk is high to moderate. The mainconcern with those zones is that field A is adjacent tothe Cato River, and field B might affect the neighbor’sfield. Based on the resulting cartography, the farmmanager could define water quality monitoring pointsor re-define management practices (e.g., less fertiliza-tion, no fertilization, lower irrigation volumes, andirrigation rates less than the soil’s infiltration capacity,

Table 10 Weights for each variable affecting solute leaching

Variable Weight Description

Water table depth 5 Distance from soil surface to water table

Drainage class 5 Gravity-driven drainage

Effective field capacity 4 Maximum soil water content available for vegetation, multiplied by soil thickness

Soil texture 3 Proportion of clay, sand, and silt

Depth of horizon A 3 Soil stratum where roots can penetrate

Water and manure application 2 Management practices for irrigation and swine manure application

Table 11 Weights for each variable affecting nutrients runoff

Variable Weight Description

Soil cover 5 Vegetation cover protecting soil against erosion

Drainage class 5 Gravity-driven drainage

Effective field capacity 4 Maximum soil water content available for vegetation, multiplied by soil thickness

Water and manure application 2 Management practices for irrigation and swine manure application

Slope 2 Average slope for each field

Soil conservation 2 Soil conservation practices associated with swine manure application


among others) to avoid or mitigate effects on thesurrounding ecosystem.

It is worth noting that field D has quite oppositerisk values for nutrient runoff (Extreme in Fig. 5a)and solute leaching (Low in Fig. 5b), because theunderlying hydrological processes are different. How-ever, because the method is biased to precautionarymanagement strategies, the integrated index of riskfor field D is Extreme. However, in fields with lowerlevels of risk, this dissimilitude is less marked,because both processes could simultaneously occur.This result shows that the method defines smoother,and physically meaningful, transitions between levels.A closer inspection of weights for the farm (Table 13)shows that the main variables affecting the environ-

mental risk are two intrinsic characteristics of thesystem, namely soil texture (loamy and sandy soils)and drainage class, and two management-relatedvariables like soil cover (fallow) and irrigationpractices. For example, in sandy soils with gooddrainage the main process is leaching. Thus, theresults show that in order to decrease the environ-mental risk it is necessary to change managementpractices (e.g., avoid fallow fields) and increase thetechnological level of irrigation systems.

The integrated index of risk (Fig. 6, Eq. 1), showsthat field B close to the Cato River has high tomoderate environmental risk levels, suggesting theneed to implement practices such as a filter strip tobuffer nutrient runoff coming from the field. Field B

Table 12 Risk levels and score range for solute leaching and nutrients runoff

Risk level Score rangesolute leaching

Score rangenutrients runoff

Description

Low 19–38 15–37 Low risk for both solute leaching and nutrient runoff.Low impact of field operations in surrounding ecosystems

Low-moderate 39–57 38–58 Swine manure application has low impact on surrounding ecosystems

Moderate 58–76 59–79 Tolerance (resilience) to occasional operational faults.Field operation must be done with care

High-moderate 77–95 80–10 Sensitive field to operational and management faults

High 96–114 101–122 Neither swine manure application nor artificialfertilization is recommended. High potential ofnutrient runoff

Fig. 3 Farm location


is particularly relevant because it is the closest field tothe river, so minor mismanagement actions could leadto immediate impacts in the Cato River and ondownstream conditions. Even though field A islocated 2 km from the river, it shows high to moderatelevel of environmental risk because of the stronginteraction between leaching processes, the directionof the groundwater flow from the terraces to the river,and hydrogeological setting of the farm. Field C has ahigh-moderate level of environmental risk (mainlyassociated with nutrient runoff) and is located in an

upstream terrace adjacent to the organic orchards. Thenatural vegetation separating both fields could allevi-ate this situation, but it does not ensure full(hydrological) isolation. Therefore, it could be pref-erable to avoid swine manure application on field ofcharacteristics such as those of field C.

The construction and use of environmental riskindices has been profusely documented in literature(e.g., Alister and Kogan 2006; Mendoza andIzquierdo 2009; Senese et al. 2010; Xu and Liu2009; Lahr and Kooistra 2010; Konishi and Coggins

Fig. 4 Farm exploitations


2008; James 2009; Smet et al. 2005). Each index isassociated with different sources of risk (e.g., pesti-cides, agro-chemicals, biological wastes), spatial scale(e.g., regional, watershed, farm), input information(from monitoring networks or public agencies), end-users (public agencies, farmers), risk thresholds, andecosystems, but they share a common goal: to provideinformation for the decision-making process in orderto reduce the risks associated with a given action or

process. In this framework, our effort provides aneasy-to-use and easy-to-adjust tool that is currentlyassisting a farmer on a seasonal basis. However, eventhough not always properly documented, the con-struction and adoption of a risk index relies on a deepand extensive analysis of available data, required data,and budgeting to define relevant processes (theconceptual model), variables (a trade-off betweencomplexity and usefulness), weights (depending on

Fig. 5 Risks index for farm. a Risk index for nutrient runoff. b Risk index for solute leaching

Table 13 San Guillermo farm case study results including factor weights and environmental risk levels

Unit A B C D E F G H I J K L M Risk leaching Risk runoff RIm

Primer 3 2 3 4 3 3 4 1 4 5 3 75 64 Moderate Moderate Moderate

Árboles 1 3 3 4 3 2 5 2 1 5 3 60 66 Moderate Moderate Moderate

Vega 1 4 3 3 4 N.apli 1 5 4 5 3 N.apli 64 Extreme Moderate Extreme

Chacra 5 3 3 4 3 2 5 2 4 5 3 75 86 Moderate High-moderate High-moderate

Final 1 1 3 4 3 2 5 4 4 5 3 83 68 High-moderate Moderate High-moderate

3sur 1 4 2 4 3 2 5 5 3 5 3 80 81 High-moderate High-moderate High-moderate

Sur A 1 1 1 – – N.apli 1 4 4 5 3 N.apli – Extreme – Extreme

13b 2 2 3 5 2 2 5 2 5 5 3 83 65 High-moderate Moderate High-moderate

Arboles 2 1 3 3 4 3 2 5 2 1 5 3 60 66 Moderate Moderate Moderate

Awater table depth, B soil depth, C slope, D texture nutrient runoff, E texture solute leaching, F drainage nutrients runoff, G drainagesolute leaching, H effective field capacity, I soil cover, J Irrigation and manure application practices, K Management practices, L totalfor nutrient runoff, M total for solute leaching


the particular setting of working unit, e.g., the farm orthe watershed), and scores (based mainly on docu-mentary data).

Additionally, there is an economic constraintrelated to monitoring. Field data is necessary tovalidate data coming from other sources (most ofthe time with a low spatial resolution), and also togenerate new data. In the case study, the farm’sprotocols consider a soil sampling campaign (ca. onesample per 10 ha, including nitrogen, phosphorus andpotassium) before seeding, after harvesting, andduring the winter. Groundwater levels are monitoredtwice a year, in March and July, the months whichcorrespond to shallow and deep levels. The nutritionalcontent of manure plus biochemical oxygen demandis controlled quarterly. To upgrade the method to abasic decision support system in a monthly or bi-weekly basis, it is necessary to control in a better waythe amount of water applied to each field within thefarm, as well as to monitor environmental variables toestimate evapotranspiration (e.g., pan evaporation).However, it is worth noting that it is possible to

improve the temporal resolution of the information,but the method was design to be applied to farmssimilar to our case study.

In this way, the general structure or architecture ofa risk index could be applied to other settings, butmost of the time it would be necessary to review,adapt or adjust both weights and scores. Thus, thoseweights were set for the particular condition of thefarm. In the study case, for instance, water table depth(see Table 10) has a higher weight than managementpractices for irrigation and swine manure applicationbecause of a Chilean regulation of disposal ofpolluted water into the soil stipulates that underconditions of shallow groundwater systems thevulnerability of the system is high and the aquifermust be protected. However it is worth noting thatcontrary to what might be expected, the existingaquifers in the Central Valley do not contain signif-icant amounts of nitrates that could be associated withagricultural activity, due to particular soil and climaticconditions that favor denitrification processes (Arumíet al. 2005).

Fig. 6 Risk Index for theuse of swine manure asbiofertilizer, RIm, for SanGuillermo farm


The typical soil heterogeneity of Chilean farmsposes a challenge in the form of different environ-mental risk levels in the same farm management unit.An option to overcome this problem is to redefine themanagement units, following the particular featuresaffecting manure application, but this option isextremely difficult to implement. The second optionis to dispose of the manure following the leastabundant nutrient, whatever the level the othersnutrients are. The third option is to follow a middlepath, i.e., a tradeoff between management practicesand intra-unit variability. For this task, risk cartogra-phy (or another ad hoc index) is a suitable tool todevelop standardized practices regarding manureapplication, and preservation of riparian vegetationacting as a buffer strip.

Concluding remarks

Very often, farm management plans assume that soilsare homogeneous units, increasing the environmentalrisk of swine manure application because appliedwater and nutrient content exceed the soil’s capacity.Water in excess infiltrates beyond the root zone orruns off, carrying soil and nutrients. Therefore, theknowledge of soil characteristics (e.g., texture, depth,water capacity) and the understanding of relevantnutrient transport processes are valuable and neces-sary information to define the level of environmentalrisk and to reduce or mitigate environmental impactsrelated to manure application.

We developed a method to assess the environmen-tal risk of swine manure application considering tworelevant processes: solute leaching and nutrientrunoff. The main advantage of our approach is thatit combines qualitative and quantitative information,including particular situations or field features basedon expert knowledge, making the method flexible andsimple. Another interesting advantage is that differentmanagement practices lead to different environmentalrisk levels so the method is a fast assessment tool.The third advantage is that the information used isreadily available, avoiding the necessity for newstudies and reducing costs. Because the informationwas generated by Chilean governmental agencies, theresults could be presented to regulatory agencies.

The proposed methodology was applied to a farmthat shows a moderate degree of environmental risk. It

is worth noting that the farm manager is currentlyusing the information produced by this research. Thelevel of disaggregation of the information facilitatesdefining field risks and differentiating the mainprocesses determining the environmental risk.

Finally, the application of easy-to-use tools forenvironmental assessment appears to be a suitabledecision support tool to make more affordableand applicable concepts associated to sustainablemanagement.

Acknowledgements We thank the financial support of theFondo de Innovación Tecnológica de la Región del Bío-Bíothrough grant 07-PC S1-198 “Generación de información parael diseño y operación de sistema de tratamiento de bajo costo yambientalmente sustentable para planteles porcinos”, andFONDECYT 11090032.

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a simple method to identify areas of environmental risk due to manure application

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