use of belief network modelling to assess the impact of buffer zones on water protection and...

Agriculture, Ecosystems and Environment 96 (2003) 119–132

Use of belief network modelling to assess the impact ofbuffer zones on water protection and biodiversity

Sirkka Tattari∗, Titta Schultz, Mikko KuussaariFinnish Environment Institute, P.O. Box 140, FIN-00251 Helsinki, Finland

Received 21 December 2001; received in revised form 15 November 2002; accepted 25 November 2002

Abstract

Bayesian belief network (Fully ConnectedBelief Networks, FC BeNe) was used to estimate uncertainty in the functioningof established buffer zones in Finland. Four experts in the field of water protection and four in biodiversity were asked to assessthe roles of 25 key variables involved in the functioning of buffer zones. The matrix comprised five levels of information: fieldproperties, buffer zone properties, management measures, site data and concerns. The eight experts were asked to estimateprobability distributions and causal relationships between the 25 variables, which included in particular erosion, particlebound P, dissolved P, and plant, insect and bird species diversity. Any of the three phases of the analysis; prior distributions,links and posterior distributions are end products which are usable as such.

The highest uncertainties were attributed to management measures, e.g. sowing leys, meadow plants, trees and/or bushesand soil removal from the upper end of the field in order to create nutrient-poor meadows. Uncertainties were also seen inP-status, fertilizer and manure changes, transport of dissolved phosphorus, particle bound phosphorus, bird species diversityand landscape. All experts thought that erosion affects particle bound phosphorus, but there was wide deviation in the linkstrength values, 0.05–0.7, which clearly reflects a lack of knowledge. Two of the experts assumed particle bound P to becomepartly dissolved, the other two did not. The effect of buffer zones on transport of dissolved phosphorus was not clear. The linkstrength direction of the variables grazing and rotational grazing to dissolved P varied between the experts.

According to the expert evaluations, positive impacts caused by buffer zones were probable in erosion, and in plant andinsect species diversity, which is in agreement with existing empirical studies. Experts expected plant height to be beneficialto bird species diversity, but disagreed as to the effect of plant height on plant and insect species diversity. Biodiversity expertsconsidered that steep slopes produced more erosion, more leaching, and hence more patches for biodiversity contrary to waterpollution.© 2003 Elsevier Science B.V. All rights reserved.

Keywords: Buffer zones; Bayesian; FC BeNe; Uncertainty; Water protection; Biodiversity

1. Introduction

Mathematical modelling is increasingly used as atool for assessing the effects of alternative pollution

∗ Corresponding author. Tel.:+358-9-4030-0240;fax: +358-9-4030-0290.E-mail address: [email protected] (S. Tattari).

control strategies. The models concerning watershedmanagement typically branch into two categories. Thefirst category incorporates goals such as increasingour understanding of the interactions and mechanismsthat affect processes within the landscape, and the sec-ond aims to develop decision support and tools forresource management. Buffer zones are designed toremove sediment and sediment bound pollutants from

0167-8809/03/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved.doi:10.1016/S0167-8809(02)00233-5

120 S. Tattari et al. / Agriculture, Ecosystems and Environment 96 (2003) 119–132

surface water by filtration, deposition, infiltration, ad-sorption, absorption, decomposition and volatilization(Muños-Carpena et al., 1999). They also have an in-fluence on the biodiversity of the agri-environmentand on the pastoral landscape (Ma et al., 2002; Meeket al., 2002). Traditional modelling attempts to clar-ify the physical processes affecting the actions causedby buffer zones. For example, permanent crop coveraffects the hydraulic conductivity of the soil and thusboth soil moisture conditions and the groundwaterdepth of the soil are altered (Beven and Kirkby, 1979;De Vries, 1994; Lowrance et al., 2000). A differenttask is to predict the effects of buffer zones on erosionand nutrient losses in different crop and soil combina-tions and varying cultivation practices (Williams andNicks, 1988; Rankinen et al., 2001).

Deterministic models cannot be used to assess mul-tidisciplinary problems. If effects on biodiversity andlandscape are included, pragmatismic modelling suchas decision analysis enables equitable management ofall different divisions. According toBunn (1984), fourproblem types can be identified within decision the-ory: uncertainty, sequentiality, non-commensurabilityand elimination of inferior alternatives. The belief net-work approach, FC BeNe (Fully ConnectedBeliefNetworks) was applied to study both state and causaluncertainty. FC BeNe can assist in managing uncer-tainty in policy making and has been already used tostudy the impacts of climatic change in Finnish wa-tersheds (Kuikka and Varis, 1997).

In Finland an Agri-Environmental Programme hassupported the establishment of buffer zones (alsocalled filter strips, filter verges or riparian zones, de-pending on their width) to reduce the loss of nutrientsand solids from fields since 1995 (Valpasvuo-Jaatinenet al., 1997). Establishment of buffer zones is alsoexpected to maintain biodiversity by increasingthe amount of seminatural open areas in agricul-tural landscapes. There are two kinds of bufferzones in the Finnish Agri-Environmental Programme(Valpasvuo-Jaatinen et al., 1997). A first level of eco-nomic support goes to farmers who leave 1–5 m wideuncultivated buffer zones along all open ditches andwider waterways. In areas with high risk of erosionand nutrient leaching the farmer may receive an addi-tional financial support by establishing buffer zones>15 m along a waterway. This paper focuses solelyon the significance of these wide buffer zones in

which pesticides, ploughing and fertilization are notallowed.

Finnish buffer zones are treeless grassy areas, inwhich plant species richness depends on the seed mix-ture used in the establishment. Usually, seed of onlyone or a few grass species are sown and plant speciesrichness tends to increase with time. Scattered treesand bushes may grow in old buffer zones. Buffer zonevegetation may be mown, grazed, or remain unman-aged for over one year. Currently, the total area of theeconomically supported buffer zones is ca. 3150 ha,but the need is estimated to total >40,000 ha in south-ern Finland (Ministry of Agriculture and Forestry,1999). In addition to protecting water quality andbiodiversity, buffer zones contribute to preserve tra-ditional open rural landscapes (Valpasvuo-Jaatinenet al., 1997).

The effects of buffer zones on erosion and nutri-ent losses are better documented than on biodiversityand landscape (e.g.Srivastava et al., 1996; Schmittet al., 1999; Uusi-Kämppä et al., 2000; Parkinsonet al., 2000). In Finland, the effects of 10 m widegrass buffer strips on sediment and nutrient losses fromcropped soil plots have been studied byUusi-Kämppäand Kilpinen (2000). Compared to plots without bufferstrips grass strips decreased loads of total solids, phos-phorus and nitrogen by an average of 60, 40 and 50%,respectively. The grass buffer strips were effective inautumn but not in spring, did not reduce dissolvedphosphorus losses and even increased it when mown.

Crop fields are species-poor compared to unculti-vated grassland habitats (Marshall and Arnold, 1995;Krebs et al., 1999) and biodiversity tends to increasewhen former crop fields are left uncultivated (Meeket al., 2002). Buffer zones >15 m represent large un-cultivated areas with potentially high significance forfarmland biodiversity. Their value in maintaining bio-diversity is likely to depend on the way they are estab-lished and managed (Feber et al., 1996; Meek et al.,2002). Whereas there is a bulk of scientific literatureon the value of different kinds of linear field verges andlarger patches of uncultivated grassland in agriculturallandscapes (Dover and Sparks, 2000; Morris, 2000;Pitkänen and Tiainen, 2001), the role of economicallysupported Finnish buffer zones in terms of biodiver-sity has not been assessed. This study therefore usedthe knowledge of water protection and biodiversity ex-perts to measure the state of knowledge on the effect

S. Tattari et al. / Agriculture, Ecosystems and Environment 96 (2003) 119–132 121

of buffer zones using the FC BeNe programme, basedon Bayes’s theorem (Varis, 1995; Pearl, 1988).

2. Method and material

2.1. FC BeNe

Many ecosystem models include uncertain variableswhich are generally interdependent. In Bayesian beliefnetworks, a prior probability distribution is assigned toeach variable and then the strength of the dependencebetween each pair of variables is defined. This depen-dence creates the causal relationships of the model.Bayes’s theorem states that, for any propositionsh ande, the posterior probability distribution is calculated as

P(h|e) = P(e|h)P(h)

P(e)(1)

where h is a hypothesis ande the evidence.P(h|e)is the posterior probability ofh on e, P(h) the priorprobability of h, andP(e|h) the likelihood ofh on e.P(e|h) can be interpreted as the degree of determinate-ness with whichh explainse andP(h) is an estimateof the weight of evidence in favour ofh beforee isknown. Posterior probability is proportional to priorprobability multiplied by likelihood (e.g.Howson andUrbach, 1991), Eq. (1)can be rewritten as

P(h|e) ∝ P(e|h)P(h) (2)

FC BeNe are based onPearl’s algorithms (1986, 1988)and present the propagation of beliefs in a fully con-nected networks (Varis et al., 1990; Varis, 1995). Allthe nodes have a directed connection to each other.FC BeNe make three amendments to Pearl’s algo-rithms, i.e. a link can be directed, negative links areallowed, and a metric for node independence is in-cluded.

The objective of belief network is the structuringof problems, expert systems and probabilistic models.The most important variables describing the systemmust first be evaluated by experts. Belief network aimsat analysing the uncertainties of a system, first by us-ing probability distributions for the variables of thesystem in question, and then by describing the interde-pendence between these variables by means of links.Links indicate how strongly the variables are interde-pendent. The more uncertain a variable, the wider its

distribution and the smaller its effect on other vari-ables linked with it. There are less known facts andmore guesses in this part of the model. This net up-dates the prior distribution inserted by experts in eachof the variables, using information from other partsof the matrix. The posterior distribution includes priorknowledge on variables and information on links be-tween variables (Fig. 1). Any of the three phases of theanalysis; prior distributions, links and posterior distri-butions are end products which are usable as such.

FC BeNe also allow for uncertainty in causal re-lationships (links) and for the uncertainty of priorprobability distributions of the variables’ states to beanalysed. A small (0.1) perturbation was made simul-taneously for all links not equal to zero and the effecton each variable was studied. The numerical measuresgiven by sensitivity analysis for each variable werecompared. In this study, the analysis was performedfor each link strength matrix. The results were anal-ysed separately for water pollution and biodiversityexperts.

2.2. Site description

The lake Kanteleenjärvi basin, located in the Por-voonjoki river basin on the southern coast of Finland,was selected to assess the effects of established bufferzones. Two types of buffer zones were considered inthe analysis: (1) zones located along waterways and(2) dry meadows between forest and field south fac-ing. Land-use classes in the catchment were: water(2.8%), agricultural field (42.9%), other open areas(3.5%), and forest (50.8%). Slopes varied between 0and 30%. Soil types were sandy clay and silty clay.Mean annual rainfall was 660 mm during 1961–1990,of which about 60% evaporated, surface runoff being200–300 mm on average. In this analysis, average cli-matic conditions were assumed. An erosion risk mapbased on GIS was provided as background informa-tion (Rankinen et al., 2001).

2.3. Data collection

A group of scientists, representing expertise for wa-ter quality, for biodiversity and for the methodology,gathered to agree on the definition of the impact ma-trix, i.e. the selection of variables to be included inthe analysis. The variables were defined as precisely


Fig. 1. Schematic diagram of the FC BeNe system approach.X-axis shows the estimated change of the variable with five different outcomes:strong negative (SN), weak negative (WN), no change (NC), weak positive (WP) and strong positive (SP).

as possible to understand their content in a similarway. The matrix comprised five levels of information(Fig. 2).

Four Finnish experts in water pollution and four inbiodiversity participated in the analysis. These scien-tists represent the best available knowledge of the im-pact of buffer zones on water quality and biodiversityin Finland. A short introduction to the problem, writtenby the authors, was sent to each of them. Each expertwas asked to write down her/his comments. The ex-perts were not interviewed personally, they were askedto contact the analysts, in case of any questions.

Experts gave first values to each pair of vari-ables in the matrix on a scale from−1 to +1; −1meaning complete negative interdependence, 0 no

interdependence, and+1 complete positive interde-pendence. The sum of the absolute values of the linksconferred to one variable would be exactly 1 if allthe factors influencing the variable were included inthe model. This information also helped to assess therelevance of the selected variables.

Experts then stated the prior probability to give theirpersonal view on the variables influencing water pol-lution, biodiversity and landscape. The prior distribu-tion described the expected direction and magnitudeof changes caused by buffer zones. The experts wereasked to estimate the changes caused by buffer zones10 years after their initiation, using a discrete proba-bility distribution in five classes (i.e. strong negative,weak negative, no change, weak positive and strong


Fig. 2. The five-level model variables: level I, field properties; level II, buffer zone properties; level III, management measures; level IV,site data; level V, concerns.

positive) for each variable of the anticipated, inducedchanges.

3. Results

Three experts criticized the insertion of P-status,which reflected the condition of traditional bufferzones, as being a conflicting variable. One biodiver-sity expert found that there should be separate P-statusvariables for both buffer zones and dry meadows.The buffer zones were assumed to be established, andtwo experts did not see any need for additional sow-ing, whereas grazing could make it necessary. Some

experts found it necessary to plant in dry meadowswhere the fertile uppermost soil has been removed.Demand for mowing was also questioned as beingdependent on P-status. One expert suggested to addsoil pH to the impact matrix, which would be a goodexplanatory variable for plant species diversity.

3.1. Erosion and dissolved P

Water protection experts felt that slope steepness,soil type and plant coverage influenced erosion andgave the highest values to these variables. None ofthe variables contributed was found to be particu-larly predominant as an affecting variable. Two water


protection experts named just three variables, whereastwo others had a large number of affecting variablesand the link strengths suggested were smaller. Oneexpert pointed out that erosion was a two-phase pro-cess in Finland, mainly occurring during snow-meltand late autumn. Field length and orientation as wellas depression areas were deemed essential and allexperts thought that erosion affected particle boundphosphorus, while there was wide deviation in the link

Fig. 3. Factors affecting and affected by: (a) erosion; (b) dissolved reactive phosphorus (DRP), according to water protection experts.

strength values, 0.05–0.7, which strongly indicates alack of knowledge on this particular aspect (Fig. 3a).

Water protection experts estimated that P-status, soilfertility, management measures, particle bound P, andsoil type influenced dissolved P. Two of the expertsassumed particle bound P to become partly dissolved,the other two did not. The link strength direction of thevariables grazing and rotational grazing to dissolvedP varied between the experts (Fig. 3b).


3.2. Plant and bird species diversity

Soil fertility and plant height decreased plantspecies diversity in the opinion of most biodiversityexperts, whereas heterogeneity of nutrients clearly in-creased it. There were conflicting opinions regardingopenness. One expert pointed out that pesticides, ma-nure, fertilizers and erosion decreased plant speciesdiversity, whereas another expert suggested that slopeincreased it (Fig. 4a).

Most biodiversity experts thought that plant speciesdiversity strongly favoured insect and bird species

Fig. 4. Factors affecting and affected by: (a) plant species diversity; (b) bird species diversity, according to biodiversity experts.

diversity, the link strength varying from 0.2 to 0.5.The number of variables affecting bird diversity wassmaller than for plant diversity. Two biodiversity ex-perts considered that insect diversity also increasedbird diversity. One expert estimated that reduced open-ness was positive to bird diversity, whereas two expertsthought that increased openness was positive (Fig. 4b).Most experts considered heterogeneity of plants to di-versify landscape. Biodiversity experts thought thatmanagement measures did not directly affect land-scape, whereas one water protection expert thoughtthat they considerably enhanced landscape.


3.3. Plant height and mowing

All experts, both water protection and biodiversity,considered that mowing, grazing and rotational graz-ing negatively effected plant height. Soil nutrients andsowing were positively correlated with plant height

Fig. 5. Factors affecting and affected by plant height according to water protection experts (a) and biodiversity experts (b).

according to five experts, sowing describing the frac-tion of trees and bushes in the buffer zone. Expertsexpected plant height to be beneficial to bird speciesdiversity, but disagreed with regard to opinions on theeffect of plant height on plant and insect species di-versity (Fig. 5a and b).


Buffer zones were to be mown once in the grow-ing season, in July–August, plant residues beingremoved. Slope and soil removal were seen as nega-tively correlated with mowing. Several variables wereperceived as negatively affected by mowing, includ-ing plant height, P-status, soil fertility and transportof dissolved P (Fig. 6a and b).

3.4. Prior and posterior distributions

The mean prior distribution was calculated as meanvalues from all the individual models (Table 1). Ex-perts were most confident about increases in plant and

Table 1Average prior distribution calculated as mean values from all theindividual models

Mean prior distributions

Negative No change Positive

Level IVegetation cover 0.04 0.60 0.34Pesticides 0.03 0.63 0.32Fertlizers 0.29 0.66 0.05Manure 0.25 0.69 0.07

Level IIP-status 0.32 0.51 0.07Soil type 0.07 0.89 0.04Slope steepness 0.05 0.91 0.04

Level IIISowing 0.16 0.31 0.53Soil removal 0.15 0.51 0.34Mowing 0.06 0.43 0.51Grazing 0.09 0.56 0.35Rotational grazing or mowing 0.05 0.58 0.37

Level IVPlant coverage 0.20 0.21 0.59Plant height 0.25 0.47 0.27Heterogeneity of nutrients 0.09 0.29 0.61Openness 0.44 0.48 0.09Heterogeneity of plants 0.02 0.16 0.82Soil fertility 0.53 0.37 0.08

Level VPlant species diversity 0.02 0.14 0.84Insect species diversity 0.05 0.11 0.84Bird species diversity 0.03 0.31 0.66Erosion 0.82 0.16 0.02Particle bound P 0.69 0.19 0.02Dissolved P 0.48 0.35 0.09Landscape 0.04 0.28 0.68

insect diversity and heterogeneity of plants. The beliefin increased bird species diversity was not as strong,and no change in bird species diversity was assumedwith a probability of 31%. A general opinion amongthe experts was that buffer zones improved visual land-scapes.

Decrease in erosion was also apparent accordingto the experts. Erosion was likely to decrease withthe persistence of established buffer zones, althoughopposite views were also expressed. This disagree-ment might indicate that some experts do not believethat buffer zones will function permanently, or theymight consider that extreme climate conditions mayincrease erosion. Biodiversity experts considered thatsteep slopes produced more erosion, more leaching,and hence more patches for diversity. The directionof the slope had a great influence on microclimateand thereby on species richness. Increased erosion in-creased micro-habitat heterogeneity and could havepositive effects on biodiversity contrary to water pol-lution. The highest level of uncertainty was perceivedin possible changes in sowing, soil removal, plant cov-erage and plant height.

The mean prior and posterior distributions togetherwith one exceptional view for seven variables aresummarized inFig. 7. Prior and posterior distributionsof erosion were similar, whereas posterior distributionof mowing diverged significantly from prior distri-butions, indicating inconsistency in the assessment.To a lesser extent, the same applied to dissolved P,plant and bird species diversity, landscape and plantheight. Some biodiversity experts considered thatmowing was needed to enhance plant species diver-sity. Another point of view is the farmers’ interestin mowing, such as for removing weeds or to sat-isfy requirements in the agri-environmental supportscheme.

3.5. Sensitivity to causalities

Sensitivity analysis revealed which variables (seeFig. 2) were most sensitive to structural uncertainty ofa model in general. Mean, maximum and minimumuncertainty of each variable are presented inFig. 8.The uncertainty in the models of water protectionexperts cumulated on the fourth and fifth levels, themost sensitive variables being soil fertility, diversityof plant, insect and bird species, erosion, particle


Fig. 6. Factors affecting and affected by mowing according to water protection experts (a) and biodiversity experts (b).


Fig. 7. Mean prior, posterior and one divergent distributions for selected variables.


Fig. 8. Measure of uncertainty (mean, maximum and minimum) of each variable according to: (a) water protection experts; (b) biodiversityexperts. Level I, field properties; level II, buffer zone properties; level III, management measures; level IV, site data; level V, concerns.

bound and dissolved phosphorus and landscape. Onthe lower model levels (I–III), fertilizers and soil re-moval were most sensitive. The uncertainty was lesssignificant in the models developed by biodiversityexperts than in those of water protection experts. Themost sensitive variables on the higher levels in themodels of the biodiversity experts were bird speciesdiversity and erosion and plant coverage, plant heightand heterogeneity of nutrients. On the lower levelsP-status and soil removal were the most sensitive.

Uncertainty of the models of the water expertsclearly increased from level I to V, whereas the dis-tribution of uncertainty among the models of thebiodiversity experts was more even. Water protectionexperts built more complex link chains and uncer-tainty increased from level I to V. For overlappingvariables (e.g. P-status and soil fertility), the links maybe blurred, increasing uncertainty inside a system.Biodiversity interest variables contained surprisinglylittle uncertainty, perhaps because of simpler linkchains in the models. Another explanation is that the

choice of the variables was made on terms dictatedby water protection experts.

4. Discussion

Personally evaluated, hence subjective, prior prob-abilities have been widely criticized (Howson andUrbach, 1991). They have, however, the advantageof taking a probabilistic approach which gives endusers a sense of the relative likelihoods of differentoutcomes. Probabilistic modelling use the experienceof experts, which is relatively difficult to inject intraditional models other than by parameter manipu-lation.

In the present study, the experts were not inter-viewed personally, which resulted in some misun-derstanding of the method used. On the other hand,the analyst was unable to manipulate experts. Theanalysis is of particular interest in terms of planningresearch allocation. Prior and posterior distributions


revealed inconsistencies in thinking. In the presentsystem, experts had to consider 25 variables to build alogical model of the functioning of buffer zones. Mostof the links were from upper to lower level and the re-verse was rare. Very few links went across more thanone level.

Most experts questioned that 10 years was longenough for the buffer zone to establish. Plant coverchanges continuously, especially for trees and bushes.Questions also arose concerning the capacity of bufferzones to persist. Extreme hydrological conditionsmight even be more important for the retention ofsolids and nutrients than physiographic variables suchas soil type and slope steepness. Slope steepness andincreasing erosion may, however, act against dominat-ing plants giving space to weaker competitors. Steephigh-erosion slopes may also benefit from a warmmicroclimate, and favour dry-condition species.

Bird species diversity is highly scale-dependent.Due to their small size in Finland, the influence ofbuffer zones on bird species diversity will remain low.If heterogeneity of vegetation increases, its effects onwater protection will be difficult to assess. Surpris-ingly, most water protection experts strongly felt thatbuffer zones did not affect the P-status of the soil.However, on dry meadows, the P-status may decreasesignificantly. Soil fertility appears to have a negativeeffect on landscape quality, probably due to mono-culture.

5. Conclusions

Experts were confident that buffer zones wouldincrease plant and insect species diversity, and birdspecies diversity to a lesser extent. Most experts feltthat buffer zones would decrease erosion. The flat-test distributions, i.e. the highest uncertainties werefound in sowing, soil removal, plant coverage andplant height. The effect of buffer zones on transportof dissolved phosphorus was not clear, and the sameapplies to manure and fertilizer levels. Posterior dis-tributions of mowing, P-status, dissolved phosphorusand plant diversity diverged significantly from priordistributions indicating inconsistency in the assess-ment and the need for additional research. This alsoshows that 25 variables were not adequately coveredby one investigator.

Relationships, e.g. link strengths gave useful infor-mation on process knowledge based on the thinkingof experts. Although the absolute numbers given byindividual experts were not comparable, the mostimportant variables and gaps in knowledge could beprioritized, e.g. erosion affecting particle bound phos-phorus, for which the link strength varied from 0.05to 0.7.

Acknowledgements

We thank the water protection and biodiversity ex-perts Jaana Uusi-Kämppä, Silja Suominen, MarkkuPuustinen, Juha Helenius, Juha Tiainen and JuhaPykälä. Special thanks are due to Olli Varis andSakari Kuikka, who taught us the Bayesian thinkingand use of the FC BeNe throughout the study. Thisstudy was partly funded by the EU/LIFE project LIFE97 ENV/FIN/335: Management of the Runoff Watersfrom Arable Land.

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