management model for decision support when applying low quality waterin irrigation

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Agricultural Water Management 98 (2010) 472–481

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Agricultural Water Management

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Management model for decision support when applying low quality waterin irrigation

M. Styczena,∗, R.N. Poulsenb, A.K. Falkc, G.H. Jørgensenc

a Soil and Environmental Chemistry, Department of Basic Sciences and Environment, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmarkb Ecology and Environment Department, DHI, Agern allé 5 2970 Hørsholm, Denmarkc Water Resources Department, DHI, Agern allé 5, 2970 Hørsholm, Denmark

a r t i c l e i n f o

Article history:Available online 19 November 2010

Keywords:Decision support systemCrop modellingLow quality waterIrrigation managementEnvironmental and health risk

a b s t r a c t

Use of low quality water for irrigation of food crops is an important option to secure crop productivity indry regions, alleviate water scarcity and recycle nutrients, but it requires assessment of adverse effectson health and environment. In the EU-project “SAFIR1” a model system was developed that combinesirrigation management with risk evaluation, building on research findings from the different researchgroups in the SAFIR project. The system applies to field scale irrigation management and aims at assistingusers in identifying safe modes of irrigation when applying low quality water. The cornerstone in themodel system is the deterministic “Plant–Soil–Atmosphere” model DAISY, which simulates crop growth,water and nitrogen dynamics and if required heavy metals and pathogen fate in the soil. The irrigationand fertigation module calculates irrigation and fertigation requirements based on DAISY’s water andnitrogen demands. A Water Source Administration module keeps track of water sources available andtheir water quality, as well as water treatments, storage, and criteria for selection between differentsources. At harvest, the soil concentrations of heavy metals and pathogens are evaluated and the risk toconsumers and farmers assessed. Crop profits are calculated, considering fixed and variable costs of inputand output. The user can run multiple “what-if” scenarios that include access to different water sources(including wastewater), water treatments, irrigation methods and irrigation and fertilization strategiesand evaluate model results in terms of crop yield, water use, fertilizer use, heavy metal accumulation,pathogen exposure and expected profit. The management model system can be used for analysis priorto investments or when preparing a strategy for the season.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

In many places around the world, fresh water is a scarceresource. Re-use of wastewater in irrigation is one option to alle-viate the scarcity and improve nutrient recycling. However, use oflow quality water for irrigation of food crops immediately raisesconcerns in consumers and authorities administering food qual-ity and health. As a result, the water quality legislation controllingthe use of reclaimed water for irrigation is quite strict in somecountries. For example, the Italian law 152/06 admits a maximumof 0.1 cfu (colony forming unit) ml−1 Escherichia coli for directwastewater use. This is considerably stricter than the WHO (2006)-recommendations of 6–7 log unit reductions of the E. coli content in

∗ Corresponding author. Tel.: +45 45 41 45 91.E-mail addresses: [email protected], [email protected]

(M. Styczen).1 Safe and high quality food production using low quality waters and improved

irrigation systems and management. Contract-No. FOOD-CT-2005-023168.

raw wastewater (108 to 1010 cfu L−1), where part of the reductionmay take place after application. To obtain such low E. coli-content,extensive and expensive pre-treatment of water is required. On theother hand, wastewater is used for irrigation in countries such asPakistan (Ensink et al., 2004) and Mexico (Scott et al., 2000) andthe resulting produce is important for local food security and as asource of income.

Several models exist that calculate irrigation water require-ment, from a number of simple water balance models representedby e.g. CROPWAT (Smith, 1992), over single-field models suchas DAISY (Abrahamsen and Hansen, 2000) or SALTMED (Ragab,2002) to decision support systems, which again range fromsimple balance models such as Pl@nteInfo® (Jensen et al.,2000), SIMIS (Mateos et al., 2002) or IRRINET (CER, 2009,http://irrigation.altavia.eu/logincer.aspx) to systems that integrateremotely sensed data for precision farming (Pinter et al., 2003).Fewer systems include nutrients and fertigation, e.g. Anastasiouet al. (2009) or Heiswolf et al. (2010). No models for calculation ofcrop water requirement were found that aim at incorporating haz-ards related to the use of wastewater, although SALTMED describeseffects of salt on crop growth.

0378-3774/$ – see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.agwat.2010.10.017


M. Styczen et al. / Agricultural Water Management 98 (2010) 472–481 473

The hazards associated with wastewater mainly relate topathogens, heavy metals and organic contaminants. These contam-inants may be hazardous to the farmers working with the waterand the consumers exposed to the produce. Particularly for heavymetals, accumulation may take place in the soil over time, thusincreasing the risk of significant plant uptake (Chang et al., 1995).WHO (2006) provides general guidelines of how to assess suchrisks. Furthermore, leaching of contaminants (McBride, 1997) aswell as the nutrients added with wastewater is an environmentalconcern to consider.

In the EU-project “SAFIR” (www.safir4eu.org), summarizedby Plauborg et al. (2010a), it was attempted to combine researchfindings from the different research groups in an irrigationmanagement model that could aid decision making regardinguse of wastewater. The project concerned itself with irrigatedtomatoes and potatoes, primary and secondary wastewater (andtap water), pathogens and heavy metals and four different watertreatment methods at six field sites. Full irrigation, regulateddeficit irrigation and the irrigation method “partial root drying”were investigated. The work with the management model forirrigation with wastewater aimed at covering the combinationsinvestigated in the field. The model system integrates analysis ofwhen to irrigate and fertigate, based on soil water content criteriaand assessment of crop nitrogen requirements, and analysesof health and environmental aspects of the applied water. Theeffect of different treatments of low quality water is simulated.Furthermore, profit calculations of the different tested scenariosare carried out. Thus, it is possible to evaluate combinations ofwater sources, water treatment methods, irrigation methods andstrategies with respect to water use, hazards and costs. The modelsystem may be used for pre-investment analysis or to evaluatea growing strategy for the next season. An earlier version of themodel concept is described in Refsgaard and Styczen (2006).

This article describes the model system that was assembled.Data obtained in the project has been used to derive the relation-ships and to calibrate sub-models; as yet the model system has notbeen validated on sites with independent data.

2. Theory and model description

The model system, which is described in detail in Styczen et al.(2009) consists of (Fig. 1):

(1) The Plant–Soil–Atmosphere model (PSA-model), DAISY(Abrahamsen and Hansen, 2000; Hansen et al., 1990). Thismodel was further developed under the SAFIR-project, asdescribed in Plauborg et al. (2010b),

(2) an Irrigation and Fertigation Strategy module (IF-module),(3) a Water Source Administration module (WSA-module),(4) a Risk Assessment module (RA-module) and(5) an Economy module.

The PSA-model is linked to the IF-module and to the WSA-module via an OpenMI-interface (Gijsbers, 2004), which is astandardized framework for linking environment-related models.OpenMI allows the user to let the prediction of one model dependon the state predicted by another model. An overview of the modelsystem is given in Fig. 1.

The cornerstone in the model system is the“Plant–Soil–Atmosphere” (PSA)-model. It simulates water, carbonand nitrogen dynamics (including crop growth) and if required,heavy metals and pathogen fate in the soil. The IF-module repeat-edly questions the PSA model for its water and nitrogen demands.In turn the IF-module requests water from the WSA-module, whichkeeps track of available water sources and their water quality,water treatments, storage, and criteria for selection betweendifferent sources. After receiving water of a certain quality fromthe WSA-module, the IF-module supplies water and nitrogen backto the PSA, according to a defined irrigation strategy. At harvest, thesoil concentrations of heavy metals and pathogens are calculatedand the risk to consumers and farmers assessed. Crop profitsare assessed, considering fixed and variable costs of input andoutput.

Using a Microsoft Excel result presentation, users get access tokey performance figures from the model simulation, including crop

Fig. 1. Overview of the management model system made within the SAFIR project. The Plant–Soil–Atmosphere model used in the project is DAISY (Abrahamsen and Hansen,2000).


474 M. Styczen et al. / Agricultural Water Management 98 (2010) 472–481

yields, water use, risk assessments and economy, but users may alsoretrieve detailed model outputs.

2.1. The plant soil atmosphere model

The PSA-model code (DAISY) is calibrated for the crop and site tobe modelled. DAISY calculates water, nitrogen and carbon dynamicsin the plant and soil. It allows several different process descriptionsfor water flow, evapo-transpiration, crop growth and solute trans-port. In total it is able to simulate about 100 different processes withabout 200 process models, implying that different process modelsare available for some of the processes. Under the SAFIR project,DAISY was extended with the possibility of two dimensional (ratherthan one-dimensional) simulations, and a framework for modellingsoil vegetation atmosphere transfer (SVAT) was added (Plauborget al., 2010b; Ragab, 2009-Annex 3.1, 3.3 and 3.6). This means thatthe exchanges of sensible heat, water vapour, and CO2 betweenthe canopy, soil, and atmosphere are simulated. Stomata openingis a function of various parameters, including abscisic acid in xylemsap, which is generated in the root system as a function of the wateruptake and pressure potential in the soil. This process is particularlyimportant when describing “partial root drying”. The SVAT modelrequires hourly weather data (precipitation, global radiation, dif-fuse radiation (if available), wind speed, vapour pressure, and airtemperature). While most sprinkler and drip-irrigation applica-tions can be simulated using the one-dimensional model, “partialroot drying” with irrigation on alternate sides of the plant is possibleonly with a two-dimensional model.

DAISY solves Richards’s equation, using data of the hydraulicproperties of the soil. Macropore flow may be specified. Further-more, data on texture, organic matter, and N-content are required.DAISY is equipped with a number of parameterised crop models,but the SAFIR project only dealt with potatoes and tomatoes. Thepotato crop model used was based on Heidmann et al. (2008) andupdated to the new SVAT-model, while the tomato models (freshand processed tomatoes) were developed during the SAFIR project(not published).

DAISY allows specification of whether the irrigation water isapplied by sprinkler or by drip irrigation to the surface or at agiven depth below the surface. Furthermore, tillage and fertil-izer or manure-additions are specified. It calculates organic matterturnover, typically including six organic pools, and the mineralnitrogen balance, including deposition, fertilization, ammonifica-tion, immobilization, nitrification, denitrification, plant uptake, andleaching. Through the OpenMI-interface, DAISY links to the IF-module.

The PSA-model supplies:

- Water content in the root zone (mm).- Water content at field capacity (mm).- Water content at wilting point (mm).- Critical N-content in the crop (kg ha−1).- Potential N-content in the crop (kg ha−1).- Actual N-content in the crop (kg ha−1).- DAISY’s crop development stage.

The PSA-model receives:

- Irrigation water to apply in the next time-step (hourly) (mm).- Nitrogen content of irrigation water, distributed on ammonia-N

and nitrate-N (mg L−1).- Heavy metal content (one or more constituent) of irrigation water

(mg L−1).- Pathogen content of the irrigation water (mg L−1). The E. coli con-

centration is used as pathogen indicator. The normal unit for E. coliwould be cfu per l (or in guidelines or legislation per ml or 100 ml),

which DAISY does not handle. To move from cfu L−1 to mg L−1 aconversion factor of 10−9 mg cfu−1 is used.

DAISY models were calibrated for the Danish (Plauborg et al.,2010b), Italian (A. Battilani, pers. com, 2009), Serbian, and Cretan(F. Plauborg pers. com, 2009) field trial sites in the SAFIR projectand were therefore available for the management model work.

2.2. The irrigation and fertigation strategy module

The IF-module receives information from the PSA-model aboutcrop development stage, soil water content and nitrogen content.Based on this and the user defined irrigation and fertigation strat-egy, the IF-module calculates how much water should be suppliedto the PSA-model and if fertigation should be added. The IF-modulepasses on the water request to the WSA module, which abstractswater from its defined sources, if available.

Irrigation can be based on calculations of the relative water con-tent (RWC) in the root zone or as a depth of irrigation water. Therelative water content is defined as (�act − �wp)/(�fc − �wp), where�act is the actual water content, �wp is the water content at thewilting point and �fc is the water content at field capacity. In theprescribed irrigation scheme option, the irrigation depth (in mm)is specified as a time series. If the demand cannot be fulfilled inone time step (due to limiting factors such as irrigation systemcapacity or water availability) the remaining demand is requestedduring the next time step. If irrigation is based on relative root zonewater content, irrigation is triggered when the relative water con-tent in the root zone, simulated by the model reaches a user definedlower limit (threshold). This lower limit, specifying the allowabledepletion, depends on the crop and its development stage and istabulated. A similar table relates upper limit values and crop devel-opment stages and is used to define the threshold for stoppingirrigation. A value of one indicates that irrigation continues untilfield capacity is reached. The depth of the root zone is defined bythe simulated root development at any given time. However, justafter planting, irrigation requirement is calculated for 20 cm depth,even if roots are shallower.

The IF-module operates with four crop development stages asdefined by Doorenbos and Pruitt (1975), and consequently also fourirrigation stages, defined for each crop, i.e. potatoes, processingtomatoes and fresh tomatoes. The irrigation stages are linked toDAISY growth stages, which in turn are linked to other types ofdevelopment as for instance the root depth, and flower and fruitformation. The irrigation start and stop trigger values used can bedefined separately for the four stages, depending on the irrigationstrategy (full irrigation, deficit irrigation, partial root drying), theirrigation method (sprinkler, drip) and the crop (potatoes, freshtomatoes, processing tomatoes). Fig. 2 illustrates this principle fortwo irrigation cases.

The threshold values for initiating and ending irrigation as afunction of crop, irrigation method, and irrigation strategy, weredetermined partly through discussions with SAFIR participants andpartly from a study of the measurements of soil water in the exper-imental plots (field and laboratory) in combination with the soilretention properties. In addition to an upper and lower threshold,“partial root drying” requires a threshold for when to change irri-gation from one side of the plant to the other. The findings fromthe SAFIR field experiments are summarized in Jensen et al. (2009)and tables of threshold values for soil water suction are availablein Styczen et al. (2009).

Fertigation is application of nutrients dissolved in water throughan irrigation system. This method may be combined with e.g.an initial application of fertilizer at planting. In the model sys-tem, fertigation is based on the N-status in the crop. Phosphorus



Fig. 2. Schematic illustration of upper and lower thresholds that change over the season. For sprinkler irrigation, the difference between the upper and lower threshold islarge while for drip irrigation, the difference is smaller. In the example “RWCUpper” is the threshold at which irrigation stops, while RWCLower-1 and RWCLower-2 are thethresholds at which irrigation is initiated for sprinkler and drip-irrigation, respectively. RWCAct-1 and RWCAct-2 shows development in actual relative water content in theroot zone as a consequence of irrigation simulated for the two cases.

and other nutrients are not considered because their dynamicsare not described in DAISY. However, the application of phos-phorus or other selected nutrients dissolved in irrigation watercan be accumulated and logged by the IF-module and the totalapplication at a given time can thus be compared to crop require-ments.

DAISY calculates N-uptake and distribution in leaves, stem, rootsand storage organs (Hansen and Abrahamsen, 2009). A certain con-centration of N is required in each of these organs to allow optimalgrowth. At any time, these critical concentrations can be multi-plied with the amount of dry matter in each organ and summedup to produce the critical nitrogen content of the plant (NCropCr).Similarly, a maximum concentration is defined for each organ, fromwhich the potential nitrogen content in the plant (NCropPt) can becalculated. The difference between the two may be interpreted asa luxury uptake or a store of N that the plant can draw on later inthe growth period. In addition, the simulated “actual” N-content inthe plant (NCropact) can be calculated and compared to the criti-cal and potential content. If NCropact lies above NCropCr the crop isgrowing optimally, and so the strategy is simply to try to fulfil thisrequirement. Below NCropCr, the plant suffers from N-stress andthe growth is retarded.

The analysis carried out by the IF-module to assess whether theplants will require addition of N through fertigation to ensure thenutritional status till next irrigation is the following:

If NCropAct >NCropCr +�NCropCr ∗ Ndays ⇒no action is required,

If NCropAct ≤ NCropCr + �NCropCr ∗ Ndays ⇒ initiate fertigation.

NCropAct and NCropCr are calculated as kg N ha−1. �CropNCr isthe daily nitrogen-requirement that will keep the crop at the criticalnitrogen level. To obtain this value, the model evaluates the dailychanges in NCropCr and uses them for the extrapolation. Ndays isthe typical time space between two irrigations. For many drip irri-gation installations this will be 1 or 2 days. The selected strategy ismost appropriate for relatively frequent irrigations; if Ndays becomelarge, the ability to predict the crop requirement becomes poorer.

The amount of nitrogen to be added is calculated as:

Addition = NCropCr − NCropAct + �NCropCr ∗ Ndays

+ “Security factor”

The security factor is defined as a user specified amount ofnitrogen (kg ha−1). It can decrease during the growing season, e.g.starting with 5 and ending with 0 kg ha−1. Fig. 4D shows an exam-

ple of the development in crop nitrogen status during the growingseason, using these principles.

If rainfall occurs there may be instances where fertigation isrequired although the relative soil water content is adequate. Thiscan potentially lead to over-irrigation. In the model, the demandfor nitrogen can trigger irrigation with the minimum quantity ofwater required to dissolve the fertilizer and avoid damage to roots,or for sprinkler irrigation, to avoid damage to the crop foliage. Ifthe irrigation water already contains nitrogen, this initial contentof N in the water is taken into account before dosing the fertiga-tion solution. Still, a specified maximum content of N cannot beexceeded.

2.3. The Water Source Administration module

The WSA-module defines the water sources available for irriga-tion (typically groundwater, river water or different waste water)as well as certain water treatment methods. Reservoirs for storageof water may be added in the model between the source and thefield to be irrigated. A flow source is characterised by its flow rate(time series) and concentrations of chemicals that the model sys-tem is desired to handle. A water treatment partly or fully removesselected chemical species or pathogens. It requires a flow capac-ity, and a factor specifying a “pass through rate” for the chemical.Simple chemical reactions can be defined as well. Water qualityfor primary and secondary wastewater used in test simulationswas based on measurements at the different SAFIR experimentalsites, particularly Italy and Crete. The treatments that were param-eterised based on results generated in the SAFIR project (Battilaniet al., 2010) were a gravel filter, a UV-lamp, and a heavy metalremoval device (a high porosity adsorber matrix based on ferrichydroxides) applied to secondary wastewater and a small-scalecompact pressurized Membrane Bioreactor, applied to primarywastewater.

A reservoir requests water from an upstream source until it isfull (or reaches a defined level), regardless of the current irrigationdemand. It is characterised by volume, outflow capacity, evapora-tion of water and decay of pathogens. While chemical reactionsmay take place in reservoirs, particularly if contaminated water isstored, this has not been included in the management model. Fur-thermore, data from the project did not allow a parameterisationof such reactions.

From each of these elements or combined elements, time seriesof the abstracted flow as well as concentrations of nutrients and,if required, heavy metals and E. coli are generated and logged. Arequest for water from the PSA via the IF-module can only be ful-filled if the flow of water though the system is sufficient.



Table 1Die-off rates for E. coli on tomatoes as a function of temperature. The reduction factor scale assumes that the rate is given at 20 ◦C.

T (◦C) 5 10 15 20 25 30Reduction factor (T0 = 20) 0.25 0.5 0.75 1 1.4 2

Days−1 k(mod) 0.1439 0.2878 0.4317 0.5756 0.8059 1.1513Days T90 16 8 5.3 4 2.9 2

Reduction per day % 13.4 25.0 35.1 43.8 55.3 68.4

2.4. Risk assessment related to pathogens

The treatment of pathogens follows to a large extent theapproaches used by WHO (2006). E. coli is used as an indicatororganism and other pathogens are expected to follow the pres-ence of E. coli in a certain ratio. Until application on the plants orsoil, E. coli numbers are calculated in the WSA module as describedpreviously. In case of sprinkler irrigation of supported tomatoes,the risk calculation is carried out as post processing based onthe concentration of E. coli in applied irrigation water and the airtemperature. Based on Ensink and Fletcher (2009), Feachem et al.(1983) and Bell and Bole (1978), the relationship between tempera-ture and E. coli die-off on plants was estimated as shown in Table 1.The temperature factor (ft) is parameterised as:

If t < 0: ft = 0.If 0 ≤ t ≤ 20: ft = 0.05 × tIf t > 20: ft = 0.1732 × e(0.07871×t) + 0.1624

The formula used for calculation of the number of E. coli presenton the fruit surface at a given time if 1 mm of water stays on thefruit after each irrigation is

Not =(

(Ct/(1 × 10−9))1000

)+ Not−1 × ekft�t

where Not is the number of E. coli in on the fruit surface (cfu), atthe time t under the assumptions given above, Ct is the concen-tration of E. coli in irrigation water in (mg L−1) added at the timet (this figure is divided by the weight of a cell (cfu mg−1) and by1000 to obtain cfu ml−1), k is the rate of decay in h−1, ft is the tem-perature modification factor and �t is the time step of 1 h used inthe DAISY output files. The calculation sums the E. coli added ateach irrigation event taking into account die-off, and the calcula-tion is carried out from the first irrigation, although no fruit maybe present. The high die-off rates ensure that the most recent irri-gations dominate the calculations. Depending on the number of

mm expected to stay on the crop, the contamination at harvest iscalculated and incorporated into the risk assessment.

For potatoes or tomatoes lying on or in the soil, contamination isrelated to the concentration of E. coli in the soil. In order to estimatethe concentration in the soil, E. coli is added with irrigation waterto the DAISY-model and is subject to die-off and filtration.

Die-off in soil was parameterised based on the review by Ensinkand Fletcher (2009) as a function of temperature and water content.The water content dependency is described as a stepwise linearfunction (Table 2). The calculated rates are used in a first orderdecay-function.

DAISY describes filtration of colloids in soil as a first order reac-tion, where the coefficient depends on the geometry of the matrix,the particle size, the flow velocity, the electrolytic compositionof the water and the surface potential on particles and pore sur-faces. Filtration in the soil matrix (micropores) is described as a1.order reaction, where the filtration coefficient can be expressedas the colloid deposition rate-coefficient (s−1) divided by the veloc-ity of colloid particles in the porous medium (m s−1), similar to theMACRO-model (Jarvis, 1994).

F = fc × v × c × �,

where F is the filtration (g m−3 h−1), fc is a reference filtration coeffi-cient (m−1), c is the concentration of the colloid in question (g m−3),� is the water content (m3 m−3) and v is the pore water velocity(m h−1). DAISY includes two micropore domains allowing two dif-ferent fc-values and no filtration in the macropores (Abrahamsen,2010).

Using parameter values found by other authors (Baun et al.,2007; Jarvis et al., 1999; McGechan et al., 2002; Villholth et al.,2000), the filter coefficient in micropores was estimated to be inthe order of 40–100 m−1 for colloid size-particles. This equals log-reduction values of 1.6–2 per m. The particles considered as colloidswere 0.2 �m in the study by Jarvis et al. (1999) and 0.02 �m for Baunet al. (2007), indicating that higher values may be appropriate forlarger colloids such as bacteria.

Table 2Die-off rates for E. coli in soil as a function of temperature and suction, expressed as T90-values and die-off rate coefficients.

Soil (pF) Factorm Temp. (◦C) 0 5 10 15 20 25ft 0 0.25 0.5 0.75 1 1.4

T900 0.6 166.7 83.3 55.6 41.7 29.81 0.8 125.0 62.5 41.7 31.3 22.32 1 100.0 50.0 33.3 25.0 17.93 3 33.3 16.7 11.1 8.3 6.04 5 20.0 10.0 6.7 5.0 3.65 7 14.3 7.1 4.8 3.6 2.66 9 11.1 5.6 3.7 2.8 2.07 11

Die-off rates (day s−1)0 0.6 0.0000 0.0138 0.0276 0.0414 0.0553 0.07741 0.8 0.0000 0.0184 0.0368 0.0553 0.0737 0.10322 1 0.0000 0.0230 0.0461 0.0691 0.0921 0.12893 3 0.0000 0.0691 0.1382 0.2072 0.2763 0.38684 5 0.0000 0.1151 0.2303 0.3454 0.4605 0.64475 7 0.0000 0.1612 0.3224 0.4835 0.6447 0.90266 9 0.0000 0.2072 0.4145 0.6217 0.8289 1.16057 11 0.0000 0.2533 0.5066 0.7598 1.0131 1.4183



The topsoil concentration of E. coli at the time of harvest is usedfor assessing the contamination of tomatoes lying on the ground.The basis for calculation of the contamination of potatoes is thecontent of E. coli at the depth of the potatoes at the time of harvest.

The actual risk assessment for consumers follows the WHO(2006) guidelines for Quantitative Microbial Risk Analysis (QMRA),using dose response curves to estimate the disease risk when theexposure is known. The concentrations and exposure figures aretransferred to Excel spreadsheets, pre-programmed with the QMRAanalysis and parameterised with default figures related to amountsof water or soil sticking to the produce (default set to 1–1.5 ml ofwater per irrigation on 100 g of tomato, 5–10 mg soil/100 g toma-toes on the ground and 10–50 mg soil/100 g potatoes) as well asconsumption of the crop in a particular country and disease relatedparameters for rotavirus, campylobacter, cryptosporidium and gia-rdia. All results of the risk analysis should be less than 10−3 torepresent an acceptable risk.

The estimation of risk to farmers when using contaminatedwater follows the same principles, except the value used for riskassessment is the range between median and maximum E. coli soilconcentration (g ha−1) in the topsoil extracted for the growing sea-son, i.e. from sowing to harvest. These values are transferred andconverted into cfu before they are included in the QMRA.

2.5. Risk assessment related to heavy metals

Like pathogens, heavy metals follow the water flow from thewater source to the field through water treatments or other definedelements in the WSA-module, as described earlier. When irriga-tion water contaminated with heavy metal ions is supplied to thesoil, the subsequent fate is calculated by DAISY using a Freundlichsorption isotherm. The approach was adopted after geochemicalmodelling of Pb at the SAFIR experimental site in Crete (Pettenatiet al., 2009) showed that this provided a good approximation ofthe data. The geochemical modelling also showed that parametersfor the lead isotherm depended on soil mineralogy, pH and con-stituents of the irrigation water. The use of sorption isotherms forassessment of accumulation of heavy metals is not new, althoughlinear partition coefficients (Kd = mass of adsorbate sorbed/mass ofadsorbate in solution) are more commonly used. Allison and Allison(2005) have provided a comprehensive review of linear partitioncoefficients for metals in surface water, soil and waste.

The model system considers five values for risk assessment ofindicated heavy metals:

1. The concentration in the irrigation water.2. The initial concentration in the soil.3. The final concentration in the soil.4. The increase in concentration due to irrigation with waste water.5. Concentration in leaching water, if relevant.

The concentration in irrigation water is evaluated in relationto the environmental thresholds given in Table 3. If the concen-tration of a heavy metal in irrigation water is below the limit forprolonged use, it is considered safe, if the concentration is betweenthe limit for prolonged use and acute toxicity, caution is required.Concentrations above the limit for acute toxicity are considereddangerous.

At the end of the simulation, the content in the root zone isevaluated. Somewhat arbitrarily, the use is considered safe if thesoil concentration is less than 70% of the maximum tolerable soilconcentration. Caution is required if the concentration is rising tobetween 70 and 100% of the maximum tolerable soil concentration,and particularly in this range, it is important to look at the increasein concentration over a growth season. The use is unsafe if theconcentration exceeds the maximum tolerable soil concentration.

Table 3Limiting contents of a number of chemical elements in irrigation water and soilbased on a literature review carried out by A. Battilani (pers. com, 2009). The figuresare a combination of WHO (2006) guidelines and other guidelines, and the resultingfigures are either equal to or stricter that the WHO (2006)-recommendations.

Element Irrigation water Soil

Prolonged use(mg l−1)

Acute phytotoxicity(mg l−1)

Concentration insoil (mg kg−1)

Arsenic 0.1 2.0 8.0B 0.5 Crop dependent,

0.5->6nd

Cd 0.01 0.05 1.0Cu 0.2 5.0 150.0Cr(VI) 0.1 1.0 ndFe 0.2 20.0 ndMn 0.2 10.0 ndHg 0.002 0.002 1.0Mo 0.01 0.05 ndNi 0.2 2.0 50.0Pb 2.0 5.0 84.0Zn 2.0 5.0 150.0

In some cases, the concentration of heavy metal in infiltratingwater or drain water may pose a problem. The concentration inleaching water is therefore presented graphically. However, if thesimulation only covers the growth season, the amount of waterleaching may be insignificant and not really represent average con-ditions for a year.

2.6. Profit calculation

The model system provides profit calculations as post-processing. The calculation, which is carried out in Microsoft Excelis simple; in short, the value of the crop yield is calculated as aproduct of the quantity produced and a (time-varying) price, andcompared to the fixed and variable costs involved in irrigation andfertigation.

The inputs required are

• The area irrigated.• Fixed costs and costs per m3 related to each water source.• Fixed costs and costs per kg N related to fertilizer and fertiga-

tion solution, including cost of application and depreciation ofrelevant equipment.

• Price of the harvested crop, which typically varies over time.

With respect to water sources, the fixed costs may be deprecia-tion of equipment of different types; while the cost per m3 could bea cost paid to the water authorities or be related to water treatment,energy or labour cost. The user can specify prices and costs; how-ever, figures implemented in the supporting tables stem from theSAFIR field sites and are reported in Pedersen (2009). The amountof water used per source is calculated in the WSA-module, whilethe amount of N used is available from the IF-module.

Typically, the price of a crop depends on the quality of the crop.A fraction of the crop has to be allocated to each quality class asthe model does not estimate quality as a function of irrigation andfertigation practices.

Particularly the price of fresh tomatoes varies with availability.For processing tomatoes the farmer usually has a contract with afixed price. For potatoes, the variation depends on the market.

2.7. Result presentation

An Excel result presentation was designed as the immediate userinterface for extracting and presenting model results. The resultpresentation consists of a number of sheets, attributed to data



Fig. 3. Screen dump of the presentation of main results in the DSS. Values related to risk assessment are shown in green, yellow or red in order to ease the interpretation.

extraction, summarizing main results, predefined time series andaccumulated plots, intermediate calculations and access to “raw”model results.

The result presentation requires the user to execute a single dataprocessing routine, which runs a number of macro’s that extractsresults from the model system and runs the risk assessment calcu-lations whereby the result presentation is populated automatically.Subsequently, the user can view a tabulated summary of the mainresults as shown in Fig. 3 as well as a range of different predefinedplots. Using Excel, the experienced model user have flexibility tocustomize and add own presentations as extensive model outputlogging is available in DAISY.

Fig. 4 shows a selection of the predefined plots in the resultpresentation. The figure shows a simplified hypothetical case ofregulated deficit irrigation in potato with subsurface drips, withaccess to clean water and secondary waste water (individual watersources not shown), but without rainfall, and a fixed groundwa-ter table 150 cm below the surface on a clay soil. Fertilizer was notapplied at sowing. The minimum irrigation frequency and the fertil-izer demand forecast are set to 5 days. Plot A shows the rainfall andapplied irrigation water during the growth season. Plot B shows thedevelopment in the relative soil water content, also indicating thechanging start and stop thresholds throughout the growing seasonof the crop. Plot C illustrates the applied amount of nitrogen fertil-izer and the actual content of nitrate-N and ammonium-N in thesoil, but also the quick uptake of nitrate-N and the slow transfor-mation of ammonium, while plot D shows the resulting nitrogenstatus in the crop.

3. Discussion

Considering the problems of scarce water resources in manyparts of the world and the benefits of recycling of nutrients it isworth evaluating how low quality water can be used safely in foodproduction. It is thought that a model system that can analyse theconsequences of application of low quality water for crop produc-tion as well as with respect to health and environment, taking intoaccount the local climate, crops, soils, irrigation techniques, agri-cultural practices, and water quality, can provide local decisionmakers, farmers, and consumers with a better basis for judgingwhere, when, and how to utilize this resource. With the possibil-ity of evaluating effects of restrictions such as application of watertreatments, restrictions of application time, or restrictions of wastewater amounts, safe scenarios can be identified and used as basisfor local implementation.

As with all model systems, the quality of the predictions dependson proper calibration and validation of the implemented model. Itmeans that the PSA-model has to be calibrated to local crop andsoil conditions in order to provide good estimates of water andnitrogen requirement during the growing season and the result-ing yields. Similarly, local data for waste water quality, sorptionof heavy metals, prices, etc. are required for the predictions to becredible. Use of the model system thus requires an initial invest-ment in local adaptation of the system by an expert user. However,as water quality and water treatments are user defined, a library ofdefault values can be included in the database of the WSA-moduleover time.



Fig. 4. Selection of the predefined plots in the result presentation based on a simplified hypothetical case of regulated deficit drip-irrigation in potato. For details, refer to thetext. Plot A shows the rainfall and applied irrigation water during the growth season. Plot B shows the development of the relative soil moisture content, also indicating thechanging start and stop trigger values throughout the growing season of the crop. Plot C shows the applied amount of nitrogen fertilizer-N and the actual content of nitrate-and ammonium-N in the root zone, but also the quick uptake of nitrate-N and the slower transformation of ammonium. Plot D shows potential (NCropPt), actual (NCropAct)and critical nitrogen (NCropCr) content in the plants during simulation. The actual N-content is kept above, but close to the critical nitrogen content during the simulation.

DAISY, the PSA-model code applied here, is an advanced modelsystem in itself and the process models used in the project requiresconsiderable skill and good data, which in many cases will be seenas a drawback to general use. DAISY can be applied in a one-dimensional version, with a transpiration calculation that dependson reference evaporation, root distribution and soil water con-tent (not including abscisic acid), for ordinary sprinkler or dripirrigation, but not for “partial root drying”, which requires twodimensions and simulation of abscisic acid production in roots.The simpler transpiration calculation requires daily weather dataand has been parameterised for several crops. The models for N-dynamics and contaminant transport are independent of the choiceof transpiration model.

When the complete model system has been parameterised byexpert users for soils, crops and climate in a given area, scenarioscan be run by computer literate agricultural engineers or similar.The end users of simulation results would be agricultural advisorystaff and farmers, but also administrators’ deciding on acceptablepractices for the use of wastewater.

The joint management of water and N, focussing on both cropgrowth and environmental effects is important. Managing theNCropAct – value to closely follow the course of NCropCr, partic-ularly towards the end of the growing seasons should optimizefertilization and minimize nitrate in the soil at final crop harvest,which should reduce the risk of leaching. However, if wastewa-ter is applied all through the growing season excess nitrate- andammonia-N may build up, increasing the risk of leaching. From

the result presentation, the user can evaluate when a shift fromwastewater to a water source with less nitrogen would be appro-priate.

The combination of irrigation modelling and detailed modellingof pathogens in soil is, to our knowledge, new. The processesselected for E. coli (die-off and filtration) mirror to a large extentthe findings from field studies as discussed in Forslund et al. (2010).In the model, E. coli can move downwards in large pores with sat-urated flow, while its movement is severely restricted in smallerpores. A consequence of this is that E. coli cannot move upwardswith evaporation from a buried drip-point to the surface. Sub-surface drip thus reduces the contamination of above-ground plantmaterial substantially. On the other hand, die-off rates may beslower below the surface due to higher soil water content and lowertemperatures than at the surface.

At present, the risk assessment scheme assumes that otherpathogens follow E. coli in a fixed ratio. If parameters for filtration,sorption and die-off are known for other pathogens, they may besimulated separately in DAISY. The evaluation of pathogen risk inthe management model system is suitable for fresh tomatoes, buttoo conservative for processing tomatoes and potatoes. Processingtomatoes are washed and heated, which reduces the contamina-tion that may reach the consumer. Peeling of potatoes decreasesthe contamination by two E. coli log-units and boiling by six toseven E. coli log-units, so the actual risk when eating the potato isextremely small and mainly related to cross contamination duringhandling of the potatoes.



The developed system is quite flexible in the sense that it canbe adapted to local requirements. It can be used to estimate irri-gation/fertigation alone or expanded to include heavy metals andpathogens as long as these can be parameterised. The profit cal-culation can be adjusted by the user if required. For example, thecost of washing produce could be added if the produce is pathogencontaminated, or the saved cost of P-fertilizer due to the use ofwastewater could be included.

There are several options for expanding the capabilities of themodel system. An obvious choice would be to generate parametersfor the SVAT-growth model and the irrigation–fertigation modulefor more crops. In particular vegetable crops would be of inter-est because they are often irrigated due to quite high crop waterrequirements and also often eaten raw or without much prepara-tion by consumers.

In addition, the model system could be developed into an on-line forecast system, where the model is updated daily with actualrainfall and irrigation/fertigation actions and e.g. a 5-day weatherforecast. As the DAISY model is able to hot-start from a saved result-file, there is no serious technical problem in doing so, except for anextra layer of complexity for the user. However, development of ashell that keeps track of model runs, result files and weather fore-casts is rather expensive and requires local interest and agreementwith a relevant supplier of forecast data.

The target group for an on-line system, where soil, crop andirrigation system information is implemented at the start of theseason would ideally be farmers, but it requires computer-literacyand time which farmers cannot be expected to have. Thereforeagricultural consultants are a more realistic target group. On-linemodelling of field trials, which in many places are the foundationof the agricultural consultants’ advice, could, for example, be aninteresting option. Near real time model outputs could provide sup-plementary information on which agricultural consultants couldrefine their advice. Design of such an online system requires a closedialogue and collaboration with the future users in order to ensurethe model system provides relevant and user-friendly informationas also highlighted by Jørgensen et al. (2007).

4. Conclusion

The management model developed is able to schedule irrigationand fertigation according to modelled soil water conditions andplant N-status. In addition it includes risk assessments for pathogenexposure and heavy metal contamination when low quality wateris used for irrigation, assisting the user in assembling safe schemesof wastewater uses.

The model system is so far an expert system with a high degree ofcomplexity, but also flexibility to adapt the model system to specificcases. However, a customized system can be applied by trainedagricultural consultants.

Acknowledgements

The management model for decision support is developedwithin the SAFIR project funded by EU (Contract-No. FOOD-CT-2005-023168) as part of the sixth Framework Programme(2002–2006). The DSS design and development builds on discus-sions with and information from all partners in the project. Inparticular thanks to project co-ordinator Finn Plauborg, Facultyof Agricultural Sciences at Århus University, Denmark, AdrianoBattilani, Consorzio di Bonifica di secondo grado per il Canale Emil-liano Romagnolo, Italy, Søren Hansen and Per Abrahamsen, Facultyof Life Sciences, University of Copenhagen, Denmark, WolfgangKloppmann and co-workers at Bureau de Researches Géologique etMinières, France, Jeroen Ensink and Tony Fletcher at London School

of Hygiene and Tropical Medicine, UK and Søren Petersen and JensErik Ørum at Food and Resource Economics Institute, University ofCopenhagen, Denmark who all have contributed significantly to theconcept and data included in model system.

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management model for decision support when applying low quality waterin irrigation

Documents

water use

water treatments

water scarcity

daisys water andnitrogen

c tuse of low quality

management model system

irrigation methods

model results