Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tnza20

New Zealand Journal of Agricultural Research

ISSN: 0028-8233 (Print) 1175-8775 (Online) Journal homepage: http://www.tandfonline.com/loi/tnza20

Estimating nutrient loss to waterways—anoverview of models of relevance to New Zealandpastoral farms

R. Cichota & V. O. Snow

To cite this article: R. Cichota & V. O. Snow (2009) Estimating nutrient loss to waterways—anoverview of models of relevance to New Zealand pastoral farms, New Zealand Journal ofAgricultural Research, 52:3, 239-260, DOI: 10.1080/00288230909510509

To link to this article: https://doi.org/10.1080/00288230909510509

Published online: 23 Feb 2010.

Submit your article to this journal

Article views: 631

View related articles

Citing articles: 13 View citing articles

New Zealand Journal of Agricultural Research, 2009, Vol. 52: 239-260 2391175-8775 (Online); 0028-8233 (Print) /09/5203-0239 © The Royal Society ofNew Zealand 2009

Estimating nutrient loss to waterways—an overview of modelsof relevance to New Zealand pastoral farms

R. CICHOTAV. O. SNOW

AgResearch—Grasslands Research CentrePrivate Bag 11008Palmerston North 4442, New Zealand

Rogerio.Cichota@agresearch.co.nzVal.Snow@agresearch.co.nz

Abstract Reliable estimation of nutrient lossesfrom farmland is of increasing interest, driven byboth economic and environmental concerns. Routinedirect measurement of nutrient losses is currentlyimpractical given the scale and variability of theproblem. Simulation models are the best alternativeand their use for assessing potential nutrient losseshas been increasing worldwide. In New Zealand,there are a considerable number of models in use,or that are being developed, aiming to estimate Nand P losses from pastoral fields. This range ofalternative models reflects both the different levelof detail and scale at which N and P losses canbe estimated, and the diverse range of purposesassumed during the model development. Thus, itis important to understand the differences betweenmodels in order to select the one that will produceestimates appropriate to the intended use. This workpresents an overview of the principal models forestimating nutrient loss being used or developedin New Zealand. It emphasises models that dealwith N and P losses from pastoral farming systems,particularly via leaching, and that may allow thehandling of different farm management procedures.Most of the models have gone through some testingand are supported by published works, althoughsome are not fully operational yet and others needfurther evaluation. There is, in general, a lack oforganised information about how several of these

A08025; Online publication date 29 July 2009Received 19 May 2009; accepted 27 March 2009

models work and what their main purposes are.We aim to supply some basic information aboutthe available tools, sorting them into categories tohighlight their primary differences and similarities.This is intended to assist discussions about modelselection as well to highlight where information gapsabout particular models need to be addressed.

Keywords Environmental policy; modeluncertainty; model selection; OVERSEER®;NPLAS;SPASMO; EcoMod; APSIM; LUCI

INTRODUCTION

Agriculture contributes to about 50% of NewZealand's export earnings (Statistics New Zealand2007), and the industry as a whole, particularlydairying, has recently been growing faster than manyother economic sectors (Ministry of Agriculture andForestry 2007; Treasury 2007). The productivity ofdairy farms in New Zealand (kg milk solids ha"1),for instance, increased by more than 15% during the1990's, resulting from gains in both the production percow and the number of cows per hectare (LivestockImprovement 2006). This gain in productivity hasbeen accompanied by increasing farm inputs. In thesame period, for example, the use of phosphorus(P) fertiliser in New Zealand has doubled and theuse of nitrogen (N) fertiliser increased five-fold(Ministry for the Environment 2006; Statistics NewZealand 2006). This increase in fertiliser inputs, andconsequently in stocking rates, may lead to elevatednutrient losses from farms.

The impact of agricultural intensification onthe environment is increasingly a matter of publicconcern, and several regulations have recently beenpassed, or are being considered, in order to limitdamaging effects on the environment (ParliamentaryCommissioner for the Environment 2004; Dragten& Thorrold 2005; Horizons Regional Council 2007).Aware of this, the industry has launched programssuch as the Sustainable Environmental ManagementStrategy (Dairy Insight 2006) to better understand

240 New Zealand Journal of Agricultural Research, 2009, Vol. 52

and reduce these impacts. Fonterra, the main dairycompany in New Zealand, and several regionalcouncils are promoting or requiring the use of nutrientbudgets to support farm nutrient management,especially for more intensive uses such as dairyfarming (Ledgard et al. 2004; Monaghan et al. 2007).Two approaches can be taken for calculating nutrientbudgets: measurement and modelling.

The impacts of agriculture in a catchment can bemeasured, for example, by monitoring the quality ofthe water bodies in the area, but the identification ofthe impact level of particular farms or the differentland uses and management practices is much moredifficult. Routine direct measurement of nutrientloss at a farm or paddock scale is currently notfeasible. While some measurement methods areavailable, their spatial or temporal scales often donot correspond with that required for monitoring.The methods can also be time consuming, costly,and generally the measures display large variability(Addiscott 1995; Oenema et al. 2003). Thealternative to measurements is the use of computersimulation models. Based on the knowledge of theprocesses involved and with support of availabledata, researchers can build models that can simulatethe farming systems. With these models, the possibleimpact of different land uses and managementpractices can then be predicted.

In light of the increasing practical importanceof nutrient simulation models, the objective of thispaper is to present an overview of the models thatmight be useful for the routine estimation of N andP losses to waterways from pastoral farms in NewZealand. As water quality is of increasing concernnowadays, we concentrate this review on the losspathways that affect water quality (leaching andrunoff) rather than on all loss pathways. We willalso focus on models that deal with pastoral farmingsystems and emphasise the impacts of farm designand management procedures on N and P losses, andthat have the potential to be used routinely across alarge number of farms. We also review a few morespecialised models that might have a supporting rolein the analysis of specific issues or could be usedin the future development of the models suitablefor routine usage. Our overview focuses on themajor models currently available and in use in NewZealand, and presents a selection of models able tohandle problems on a wide range of scale, detail, anduncertainty. With this overview we expect that theparties interested in using modelling tools or theirresults, whether for management, research, or policymaking, will have a guide for reference.

B A c K g r o u n d

Modelling is an important method for compre-hensively integrating the knowledge of basicprocesses and describing a system beyond thatwhich can be accomplished using subjective humanjudgments (Bywater & Cacho 1994; Hutson 2003).Estimation of production, irrigation, nutrient balance,and leaching of chemicals are some of the subjectareas where models have most frequently beenapplied in agricultural management.

Several different modelling approaches have beenused to evaluate nutrient losses from farm systems,covering a broad range of scale and purposes, withvarying levels of detail and uncertainty (e.g., Di &Cameron 2000; Close et al. 2003; van Beek et al.2003; Elliott et al. 2005; Shorten & Pleasants 2007;Vogeler et al. 2007; Johnson et al. 2008). While allthese models produce an estimate of the nutrientbalance of the system or area, their complexitycan vary greatly, according to the developmentapproach and assumptions, and the intended usage(Addiscott 1995; Boote et al. 1996; Rykiel 1996;Cichota & Snow 2008). The varying level of detailin these models is mostly regarding the numberof pools and processes considered in the balance(Fig. 1 and 2). The main difference between themodels is how each item in the nutrient balance isestimated. The estimates can be produced in somemodels using complex mechanistic, or process-oriented, descriptions of a reasonably large numberof processes involved in the nutrient dynamics (Fig.2). Conversely, there are models that use simpler,typically empirical, descriptions of those processes,and may take fewer items into account (Fig. 1).

Simple models are typically associated with largespatio-temporal scales, for example annual averagesof nutrient losses on a paddock, farm, or catchment(Fig. 1). The description of the system in such modelscan be simplified because the variability of manyprocesses tends to decrease at large scales (Beven1989; Addiscott 1996; Lin et al. 2005). To trackthe variation of processes at small scales, however,greater detail must be included in the model (Fig.2) and so the model becomes more complex. Themore complex the model, the more informationabout the system is usually required for obtaining itsparameters. However, these models can usually be setup with greater specificity, and although specificitydoes not directly imply accuracy, it certainly effectsthe perception of the user and their subsequentconfidence in the model (Brown & Bewsell 2008).It should be noted that although there is no hardboundary separating simple and complex models,

Cichota & Snow—Estimating nutrient loss from pastoral farms

Firm

241

Fig. 1 Simplified schematic of anutrient balance over a large scalearea (farm system).

Inputs

Atmosphere

^ haddocks P

6 inputs

Supplement*

Productsintedt vraH. rtiilk, .h

Losses

Fig. 2 Schematic of the cycleand processes involved in the nu-trient balance in a pastoral field.

this terminology can be helpful for the discussionsin this work.

Complex models, describing natural and managedsystems with great detail, are common tools forscientific research and consulting (Addiscott & Tuck2001; Beven 2002; Keating et al. 2003). Althoughthere is large variation between these models,their basic characteristics include the capacity towork at various temporal and spatial scales (smallscales in particular), to handle a large variety ofprocesses, and may include simulations of complexfarm management. Simple nutrient budget modelshave become common tools for the analysis andmanagement of farming systems on a basic level.These models have also been used for generalevaluation of the sustainability of production andenvironmental standards of farms, catchments,and even countries (Goodlass et al. 2003; Kutra &Aksomaitiene 2003; Schlecht & Hiernaux 2005;Gourley et al. 2007). Although a nutrient budget

does not provide detailed information, it is an easy,simple, and flexible tool for estimating the amountof nutrient available or that is required to sustain aproductivity level (O'Connor et al. 1996; Scoones& Toulmin 1998; Oenema et al. 2003).

The use of simulation models in New Zealand hasbeen accelerating in recent years, as their importancefor research and environmental analysis is recognised.Measuring nutrient loss at an appropriate scale forassessment is time consuming, costly, and subjectto large variability. Modelling can play a key roleto overcome these challenges.

There is an apparent overlap of what seems alarge number of models available or being developedfor this purpose (Dairy Environment Review Group2006; Brown & Bewsell 2008). This is a result of thedifferent levels of detail and scales at which N and Plosses can be reported. Also, some scepticism seemsto arise from the lack of information about the basisand validation of these models (Brown & Bewsell

242 New Zealand Journal of Agricultural Research, 2009, Vol. 52

2007,2008). Because different models are appropriatefor different purposes, it is important to know whatthey can and cannot do compared to the users' needs,so that the most appropriate model is selected.

M o d e l s F o r FArM n u t r I e n t l o s se s t I M A t I o n u s e d I n n e w z e A l A n d

The most relevant models being used or developedin New Zealand to compute nutrient balancesin agricultural fields are listed in Table 1. Thesemodels can be used to obtain estimates of lossesat various scales, taking into account different landuses and management practices. Given the rangeof alternatives, it is important to know both thestrengths and the limitations of these models in orderto choose the best tool for a given task. Initially, twogroups of models for estimating nutrient losses atpaddock/farm level are identified. These are simpleraverage type models and the more complex dynamicsystems models, which are discussed below. Somemodels for larger scales or higher level of detail arealso shown in Table 1, but will be discussed later.

long-term average type models(nutrient budgets)These models focus on calculating the balanceof nutrients on a long-term annual average basis.

The scale of their predictions is paddock or farm,and most of the processes have relatively simple andempirical descriptions. Two models in this categoryare discussed, both developed and in use in NewZealand, and which demand only a moderate levelof expertise to use.

OVERSEER®

The OVERSEER® model (Ledgard et al. 1999;Wheeler et al. 2003,2006) uses empirical relationships,internal databases, and readily available data froman "existing" farm to estimate the nutrient inputsand outputs at farm or paddock scale, and presentsthem as a nutrient budget. Here an "existing" farmrefers to the fact that OVERSEER® does not simulateproduction but instead requires farm productivityand farm inputs (fertiliser, supplements) as inputs tothe model. These quantities are usually known forexisting farms or can be estimated for hypotheticalfarms using models such as Farmax (Marshall et al.1991; Webby etal. 1995) or Udder (Larcombe 1999).Using the nutrient budget and indices derived fromit, such as farm nutrient efficiency, the model canbe used to examine the impact of nutrient use andflows within a farm. Both nutrient use efficiency andenvironmental impact are assessed, and the effectof implementing some mitigation options can beinvestigated (Wheeler et al. 2003, 2006; Ledgardet al. 2004).

table 1 Summary of several models available for estimating nutrient loss from pastoral farms. M, mechanistic;P, process-oriented; E, empirical; Q, quasi-empirical; P/p, Point/paddock; P/f, Paddock/farm; F/c, Farm/catchment;C, Catchment.

Model

daily or sub-daily time-stepAPSIMCrop calculatorsDNDCEcoModGLEAMSHYDRUSLEACHMLUCIRoTaNSPASMOAnnual average estimatesAquiferSimCLUESEnSusNLENPLASOVERSEER®SPARROW

Main subject

BiophysicalPlant physiologyBiochemistryBiophysicalBiogeochemistryBiogeochemistryBiogeochemistryBiophysicalNlossBiogeochemistry

N in groundwaterN and P lossN leachingN leachingN and P lossN budgetN and P loss

Type

PPPPPMPPEP

QQEEEEQ

Scale

P/fP/pP/pP/fP/pP/pP/pP/fC

P/p

cF/cF/cF/cP/fP/fC

Primary reference

Keating et al. (2003)Li et al. (2007a)Saggar et al. (2007b)Johnson et al. (2008)Leonard etal. (1987)Simunek et al. (2005)Hutson (2003)Jamieson et al. (2006b)Rutherford et al. (2006)Green et al. (2004b)

Bidwell & Good (2007)Woods et al. (2006)Stephens et al. (2003)Di et al. (2005)EBoP (2007)Wheeler etal. (2006b)Schwarz et al. (2006)

Cichota & Snow—Estimating nutrient loss from pastoral farms 243

The model has been developed reviewing theknowledge obtained primarily in New Zealand andin consultation with end-users (farmers, consultants);thus it is well suited for handling managementpractices and environmental conditions particularto New Zealand. OVERSEER® has been specificallydesigned to require minimum input with data thatare meaningful to farmers and easily obtained(AgResearch 2007). For reliable performance, theOVERSEER® model requires that reasonable inputdata are given (Wheeler 2009). This implies, forexample, that the amount of fertiliser required tosupport the given level of production needs to beknown. It is also assumed that the system is in quasi-equilibrium and that good management practicesare followed (Ledgard et al. 1999). The model isdesigned to predict the long-term average behaviourof the system and so it is not suitable for examinationof extreme-case scenarios or systems in transition.Likewise, it is not suitable for estimating nutrientlosses from particular years.

OVERSEER® has three major sub-programsor modules: pastoral, cropping, and horticultural.Currently the model's strength lies in the pastoralmodule, where more research and data for calibrationare available in New Zealand. Work to strengthenthe cropping and horticultural modules is currentlyunderway (Whiteman & Brown 2009). From the earlyversions, where the nutrient budget comprised N andP, the model has evolved to include several othernutrients and pH. Also, an estimate for greenhousegas emission (CO2, N2O, and CH4) using inventorymethods is now given (Wheeler et al. 2003, 2006).

Initially OVERSEER® was primarily used toassist fertiliser management, but it has evolved tobecome a tool for evaluating farm systems, includingits impact on the environment (Wheeler et al. 2006).OVERSEER® is widely used in New Zealand as adecision support model by consultants. Training inthe usage of the model has been integrated into a studyprogramme on sustainable nutrient management atMassey University (FLRC 2008). OVERSEER® hasalso been used in a series of studies for evaluatingdifferent systems and scenarios, for comparingnutrient efficiency of New Zealand farms withoverseas counterparts (Ledgard et al. 2000; Thomaset al. 2005), and to examine the effects of land usechange and management practices on nutrient loss(Condron et al. 2000; Ledgard et al. 2001; Ledgard &Power 2006). More recently the model has also beenused to monitor farm nutrient losses as an instrumentfor applying new environmental policies (Dragten &Thorrold 2005; Horizons Regional Council 2007).

NPLAS (Nitrogen and PhosphorusLoad Assessment System)

NPLAS has been developed by the National Instituteof Water and Atmospheric Research (NIWA) inconjunction with Environment Bay of Plenty (EBoP)and AgResearch. NPLAS is intended as a tool toestimate N and P loss, either to streams or to theground water, from properties in the Rotorua Lakescatchment. NPLAS uses empirical relationshipsderived from the OVERSEER® and GLEAMS(Knisel & Davis 2000) models calibrated to theRotorua catchment to describe the effect of farmingsystems on nutrient loss (EBoP 2007). It calculateslong-term averages of nutrient loss based primarilyon land use, with minimal or generic information onfarm management. NPLAS (EBoP 2007) accountsfor various land uses, including pastoral farming,cropping and horticulture, and also forestry, nativebush, recreational, and urban uses. The model allowsinvestigation of the impact of protection features, suchas fencing waterways and the presence of wetlands,on nutrient loss. These features are set at differentqualitative levels of effectiveness, which were setbased on literature values and general knowledge(EBoP 2007). A free version of NPLAS was releasedin 2006, and can be operated via the internet runningfrom the EBoP web server. This model was releasedfor tests and further development was planned (D.Ede, 2007 pers. comm.). However, upgrades madeon OVERSEER®, including most of the differentialfeatures of NPLAS, such as the consideration ofwetlands, make it likely that NPLAS will be phasedout in favour of OVERSEER® (S. Elliot, 2008 pers.comm.).

dynamic paddock and farm system modelsIn this category four model frameworks are reviewed.These models work with complex systems on dailyor sub-daily time steps and are capable of simulatingquite different systems by using different sub-modelsor modules that handle the specific processes. Allof these models are suitable for simulating someaspects of New Zealand farm systems, however thefocus and strength of each model are quite different,particularly when considering their potential tomodel whole farm systems. The areas of overlapbetween these models are only partial.

SPASMO (Soil-Plant-Atmosphere System Model)

SPASMO (Green et al. 2003a, 2004b) is a detailedprocess-orientated model developed by HortResearch(now Plant and Food Research) for simulating theinteractions in the plant-soil-water system. It has

244 New Zealand Journal of Agricultural Research, 2009, Vol. 52

been developed using well established internationalscientific knowledge, but has been adapted and testedusing research developed in New Zealand conditions.Early versions of the model have been used sincethe late 1990s, and it has been continually improvedwith the implementation of more detailed routinesand the addition of procedures to handle the variousprocesses in the soil.

Water flow through the soil is simulated inSPASMO using a water capacity approach (Hutson& Wagenet 1993), and allows the specification ofa mobile and an immobile fraction (Addiscott &Whitmore 1991). The model has a simple routine tosimulate plant growth, similar to that of Eckersten& Jansson (1991), and plant water uptake that canbe adapted to one of several different crops rangingfrom pasture to vegetables to kiwifruit vines. Thetransport of nutrients is estimated after computingthe outputs of processes such as fertilisation, plantuptake, volatilisation, exchange and transformationin the soil. Most of the processes in the soil aresimulated assuming first-order relationships, andcan have weighting factors to account for abioticinfluences, such as temperature and soil moisture(Green et al. 2003a).

SPASMO has been used mainly for horticulture,where usually only single paddocks are considered. Itis possible to set up the model to simulate variabilitywithin the farm (Green et al. 2007), however, themodel itself does not deliver outputs for the wholefarm. This integration has to be made indirectly, post-simulation, by the user by combining the outputsfrom several model runs (S. R. Green, 2007 pers.comm.). A set of rules defined a priori are used tocontrol fertilisation, irrigation, and other managementpractices for the simulated paddock. For pastoralsimulations, the model uses rules for grazing/stockingfollowing a pre-defined schedule, with supplementbeing brought in if feed is insufficient. Nutrientsexcreted by animals are the result of a balance betweenthe intake and the requirements for maintenance,growth, and production. The remaining nutrients arereturned to the soil as urine and dung and are assumedto be uniformly distributed over the paddock.

Being a process-oriented model, SPASMO canbe made quite specific to a particular area providedthe necessary soil and weather conditions are known.This can provide some advantage over the simplernutrient budget models, but the cost of this specificityis that the model needs more input data. However,in this respect, SPASMO is still simpler than someof the soil process models presented below (Sarmahet al. 2006).

SPASMO is a flexible model framework; theaddition of different modules enables it to be adaptedfor specific systems, but it does not have a welldeveloped end-user interface. It is, thus, an expert-user model and is not directly available beyondHortResearch. The SPASMO model has been widelyused in research, such as in the evaluation of Nleaching from pastoral and horticultural land (Greenet al. 2000, 2003a; Rosen et al. 2005), estimationof water use by plants (Green et al. 2003b, 2004a;Vogeler et al. 2004), and assessment of pesticidetransport in soils (Close et al. 2003, 2006; Sarmahet al. 2005). The GROWSAFETM calculator(Snow et al. 2004), a tool developed to evaluaterisk of pesticide leaching and residual build-up inagricultural soils, has been created using estimatesof pesticide dynamics simulated by SPASMO usingan extensive combination of crops, regional climatesand soil types across New Zealand.

EcoMod

EcoMod is a biophysical model designed to simulatepastoral systems of New Zealand and Australia(Johnson et al. 2008). The farm is subdivided into auser-defined number of paddocks where attributessuch as soil properties, pasture species, irrigationand fertiliser management can be defined. Themodel integrates these paddocks into a farm bycontrolling the grazing of a mob of animals aroundthe paddocks, with supplements made or fed outdepending on pasture supply and the animal feeddemand. EcoMod is able to simulate dairy, beef,sheep, and deer systems. Water and nutrient processesare simulated in detail within each paddock. Waterbalance, including runoff and leaching, and nutrient(N, P, K, and S) and organic matter dynamics aresimulated by specific modules. Farm performance andnutrient losses are estimated, including greenhousegas emissions (Johnson et al. 2004). EcoMod, andits partner DairyMod, have evolved from the SGS(Sustainable Grazing Systems) model (Johnson et al.2003) and so has its strengths in pastoral systems.

EcoMod accounts for several processes with arelatively high level of detail, thus a large amountof input data is required to set up a farm simulation,similarly to SPASMO. The user interface of Ecomod,however, is comprehensively developed. Nonetheless,EcoMod should be considered a research model ratherthan a decision support tool. EcoMod can be setup to investigate the implications for urine nutrientreturn into patches rather than the uniform distributionthat is commonly assumed in most process-orientedmodels (Snow et al. 2009a,b). To date, model usage

Cichota & Snow—Estimating nutrient loss from pastoral farms 245

has included testing against measured dryland andirrigated pasture growth rates in the South Island(White et al. 2008) and testing of pasture growthagainst several Australian and New Zealand datasets(Cullen et al. 2008). EcoMod was also used to explorethe potential for several leaching mitigation optionsfor the Lake Taupo catchment (Bryant et al. 2007,2008). The model is being adapted to fit into theAPSIM modelling framework, described below (R.J. Eckard, 2008 pers. comm.).

LUCI, the Crop Calculators, andFarmSim

LUCI (Land Use Change and Intensification) is amodel framework for simulating, at a paddock scale,changes in drainage, and N leaching from differentland uses and management systems (Jamieson etal. 2006b; Zyskowski et al. 2007). This model, stillunder development, can predict plant growth andnutrient leaching. It is based on the crop calculatorsdeveloped by Crop and Food Research (now Plantand Food Research). The Crop Calculators (Li etal. 2007a), already being used in New Zealand andthe United States, have an easy-to-use interface thatallows land managers to investigate tactical irrigationand fertiliser management in their paddocks. Thesecalculators focus on a single cropping season.

The LUCI model dynamically simulates plantgrowth, mineralisation and C and N processes in thesoil and the key components of the water balance.LUCI is designed to track these processes overseveral years. The Sirius Wheat Model (Jamiesonet al. 1998; Jamieson & Semenov 2000) was thestarting point for the development of LUCI but themodel can now simulate several other crops, suchas potato, maize, peas, and forage brassica (Wilsonet al. 2004, 2006; Zyskowski et al. 2004; Jamiesonet al. 2006a; Li et al. 2006). A module for ryegrass/clover pasture is under development (Snow et al.2007c) and methods for implementing variabilityassociated with urine patches are being investigated(Snow et al. 2007b).

A collection of LUCI paddocks can simulate afarm. This integration is done by FarmSim (Lilburneet al. 2006), and includes a description of farmmanagement. FarmSim is intended to account for theeffect of integrating different land uses and supplyinginformation to a groundwater simulation model,AquiferSim (Bidwell & Good 2007). AquiferSimthen integrates the outputs of the various farms toestimate the effect of farm management on groundor surface water. With this integration the model isuseful for crop and pasture management at smallscales as well as for environmental monitoring and

policy analyses at larger scales (Jamieson et al.2006b; Lilburne et al. 2006). LUCI and FarmSimform part of the suite of tools being developedwithin the Integrated Research for Aquifer Protectionresearch programme (IRAP 2008).

APSIM (Agricultural Productionsystems SIMulator)

This model framework has been developed by theAgricultural Production Systems Research Unit inAustralia and is designed to simulate biophysicalprocesses of farming systems (Keating et al. 2003;APSRU 2008). APSIM is a flexible platform forstudying the impacts on agricultural production,economics, and environmental outcomes caused bychanges in the climate and/or in the managementsystem.

The framework of APSIM comprises severalbiophysical modules to simulate specific processes,a series of management modules to account for thevariation in the farm management, an input/outputmodule to interface with the user, and an engine thatlinks and controls these modules (Keating et al. 2003).In the APSIM framework, the user can choose themodules to be used, and when based on the CommonModelling Protocol (Moore et al. 2007) differentmodules can be easily added as they are developed.These new modules can be developed independently byresearchers interested in using APSIM's managementscripting module or any other module. This frameworkgives considerable flexibility to set up simulationsand allows the re-utilisation of modules for differentjobs, avoiding overlaps and saving development time(Holzworth et al. 2009). APSIM modules developedso far are mostly based on cropping systems (APSRU2008) but there are modules able to handle pastoraland natural vegetation systems being developed (Huthet al. 2001; Whitbread & Clem 2006). The amountof input data required to set up APSIM simulationsdepends on the way the individual selected moduleswere built, but a relatively large number of parametersis generally necessary. Consequently, until now thismodel has mostly been used for research and consulting.Recently a simplified interface (Yield Prophet®) hasbeen released in Australia for use by farmers (Huntet al. 2006). APSIM's application in New Zealandis still limited, but increasing. Publications includesimulations of drainage and runoff in tile-drainedsoils (Snow et al. 2007a) and some studies on climatechange impact on plant growth (Asseng et al. 2004).In addition, the APSIM framework is being used toadd management flexibility to the EcoMod model (S.Rains, 2008 pers. comm.).

246 New Zealand Journal of Agricultural Research, 2009, Vol. 52

M o d e l s F o r n u t r I e n t l o s se s t I M A t I o n A t d I F F e r I n g s c A l e s

catchment modelsThe scale of a catchment is typically larger thanthat of a farm, and generally several farms areenclosed within the catchment area. Environmentalassessments and policies are often defined, however,at catchment scale as they are natural partitionsof the environment with respect to water flow.Therefore, catchment models are of interest mainlyto regional councils and national governmentalauthorities. There is considerable variation in thescope of catchment models. Typically these modelsdeal with a simplified level of detail regarding thedifferent land uses, however, they should includeall the significant land use types present in the areaand should deal with point sources of nutrient loss(Alexander et al. 2002b; Schlecht & Hiernaux 2005).These models also may handle groundwater flowand attenuation of nutrients in the groundwater andstreams. Because the effect of the different landuses may depend on their relative position in thecatchment (e.g., Refsgaard et al. 1999; Alexander etal. 2002a; Bidwell et al. 2005; Schlecht & Hiernaux2005), these models are often built coupled witha mapping tool, such as geographic informationsystem (GIS). The temporal scale of catchmentmodels varies depending on the processes beingconsidered and its purpose.

Some of the models presented in this categorycan also produce nutrient balances at smaller scales,but this has not been their primary use. All modelshave been developed or calibrated in New Zealandto simulate catchments at varying scales.

EnSus (Environmental Sustainability)

EnSus is a framework model for assessing andmapping the relative risk that different land usesrepresent to soil and water quality (Stephenset al. 2003). This model combines maps of soilvulnerability with land use pressure to produce riskand management maps. Economic and social valuesare also included (Hewitt & Stephens 2002). All ofthe information stored in a database is linked to aGIS to produce the maps (Stephens et al. 2002).The model has been prepared for use at catchment,regional or national scales, although it is possibleto use it at smaller scales provided the appropriatemaps are supplied. The vulnerability of a soil typefor N loss is estimated by the difference between thepotential leaching from the soil and the attenuationfactors applied during transport to the groundwater

or water body (Woods et al. 2006). Potential leachingis obtained by combining a soil permeability factorand a climate factor (rainfall to evaporation ratio,plus an index of available water), while attenuationfactors represent the occurrence of denitrification inwetlands and anaerobic soils. In EnSus, it is possibleto incorporate expert knowledge within the databaseinformation to make predictions of the risk for somedefined hazards.

The EnSus framework is appropriate for analysisof risk where good mechanistic models are lacking,and/or the data is patchy. Its use is still limited, butapplications are set to increase as it will be incorporatedinto the CLUES package as described below.

NLE (Nitrogen Leaching Estimation)

NLE is a semi-empirical model designed to produceestimates of the annual averages of N leachingfrom different land uses into the ground water.In its first version, the model used an empiricalrelationship between the potentially leachable N andthe concentration of N in the drainage water (Di &Cameron 2000). The potentially leachable N wasdetermined by balancing the annual fluxes of themajor N cycling processes. Empirical or functionalrelations for each of the annual N fluxes weredetermined in experiments, either using lysimetersor in the laboratory. The relationship betweenthe amount of N that is potentially leachable andthe amount that actually leaches was empiricallydetermined and tested against published data (Di &Cameron 2000). The differences between land usesand management are accounted for in the nutrientbalance. A newer version of NLE estimates theamount of N leached into the ground water in a givencatchment area by computing the relative effect ofthe different land uses (Di et al. 2005). A different,but constant in time, N concentration in the drainagewater is assumed for each land use. The amount ofwater leached is estimated by a water balance.

This model has been developed at LincolnUniversity and tested against data from Canterbury.The model would need calibration for applicationin other regions. NLE could be used at farm orcatchment levels but its use is not widespread (H.J. Di, 2007 pers. comm.).

SPARROW(SPAtial Referenced RegressionOn Watershed attributes)

The SPARROW model is a semi-empirical modeldeveloped in the United States for estimatingnutrient yield in catchments and the load at itsdischarge point (Alexander et al. 2002b; Schwarz

Cichota & Snow—Estimating nutrient loss from pastoral farms 247

et al. 2006). The model uses a mechanistic structureto correlate nutrient flux in streams and spatial dataon nutrient sources, landscape characteristics, andstream properties. Landscape is characterised bysoil permeability, drainage, and temperature. Thenutrient sources (land uses or point sources) aredefined by specific coefficients (Alexander et al.2002a). The relationships within the model must becalibrated to the catchment in study.

SPARROWhas been adapted by NIWAto estimateN and P loads from most catchments throughoutNew Zealand (Elliott et al. 2005). However it hasnot been validated for very small catchments. As thisis a large scale model, SPARROW is primarily usedto assess the status and performance of catchments(Alexander et al. 2002a). It is also being incorporatedinto the CLUES framework.

ROTAN (ROtorua and TAupo Nitrogen)

The ROTAN model was developed to estimatenitrate leaching to groundwater and then to streamsand further to the lakes of the central North Islandof New Zealand (Rutherford 2005; Rutherford etal. 2006). This proprietary model was proposedafter increasing concern about the quality of thewater bodies of this region. The model simulatesthe balance of the main water inputs (rainfall), thetransfer processes (infiltration, percolation), andoutputs (evaporation, streamflow). Streamflow iscalculated from the outflow from three conceptualreservoirs (Rutherford 2005), representing quickshallow sub-surface flow (time scale 1-2 days),slow subsurface flow (2-10 days), and groundwater(weeks to years). Two different approaches are beingtested to estimate nitrate loads into groundwater andstreams, but the nitrate loss from the soil for differentland uses need to be given and OVERSEER® andNPLAS are initially being used for this purpose(Rutherford et al. 2006). The model is being validatedwith the collection of new data (K. Rutherford, 2007pers. comm.).

CLUES (Catchment Land Useand Environmental Sustainability)

CLUES is a model framework which combinesOVERSEER®, SPARROW, SPASMO, EnSus,and HC model (an economic model from HarrisConsulting) to predict environmental and economicimplications of land use or management changes(Woods et al. 2004, 2006; Semadeni-Davies et al.2006). The CLUES-GIS framework can be iterativelyhandled by users to specify land uses and to producemaps of nutrient yields, leaching, and economic costs.

Currently CLUES is able to predict annual averagesof N and P yields and losses at sub-catchment scale(Semadeni-Davies et al. 2006). Several land uses canbe selected, and different scenarios can be compared.Not all of the components are presently integratedwithin the CLUES framework; the model is stillin development and will require further validation(Woods et al. 2006).

AquiferSim

AquiferSim the key modelling tool for aidingenvironmental policy analysis that has emergedfrom the IRAP research programme (Lilburne etal. 2006; IRAP 2008). AquiferSim is a regional-scale groundwater model designed to evaluate theeffect of N leaching on the quality of the underlyinggroundwater (Bidwell & Good 2007). The inputinformation required includes climate data, landuse, and aquifer properties, and is given in the formof GIS layers, at a 1 ha spatial resolution (Bidwellet al. 2005; Bidwell & Good 2007). The surfaceinputs are used to reference a lookup table of annualaverage drainage and N leached from the root zoneof the appropriate land use. These values have beenobtained from experimentation or an appropriatesimulation model.

To evaluate, in few minutes of simulation, theeffects of future land-use scenarios at a scale ofseveral thousand square kilometres AquiferSimuses various scaling strategies. These range fromthe development of a dedicated steady-state model(Lilburne et al. 2008), to a series of specificsimulation exercises that identified key processesand factors, and appropriate simplifications(e.g., Snow et al. 2007b). AquiferSim is still ondevelopment, and is to be installed at EnvironmentCanterbury. Council staff will be able to runsimulation in order to determine the likely effectof proposed land-use rules on nitrate leaching anddrainage and then on groundwater quality.

soil process modelsThe models under this category are all process-oriented, although the degree of complexity variesamongst them. These models were developedprimarily for analysing soil processes. Detailed plantand animal processes are mostly beyond the scope ofthese models and the effect of different managementon the nutrient balance is generally assessedindirectly. These models have been developed inthe United States but are recognised worldwide andhave been applied in New Zealand.

248 New Zealand Journal of Agricultural Research, 2009, Vol. 52

GLEAMS (Groundwater Loading Effectsof Agricultural Management Systems)

GLEAMS was introduced in the late 1980s, havingbeen developed as an extension of the CREAMS(Chemicals, Runoff and Erosion from AgriculturalManagement Systems) model (Leonard et al. 1987).GLEAMS simulates the loadings of water, sediment,pesticides, and nutrients at the bottom of the root-zone from a homogeneous field, based on complexclimate-soil-management interactions (Knisel &Davis 2000). It was developed to evaluate the impactthat differing management systems, such as croppingrotations, irrigation, and tillage operations, haveon the potential for chemical leaching. GLEAMSuses a tipping-bucket approach to simulate watermovement in the soil, and its solute movementalso uses simpler approaches than the other modelsdescribed below.

The GLEAMS model has been used in manystudies on water and solute movement worldwideand it is part of the USDA-NRCS repertoire ofoperational models. In New Zealand, GLEAMS hasbeen used to simulate N mineralisation and leachingin Canterbury soils (Webb et al. 2001; Lilburne &Webb 2002; Lilburne et al. 2003), and to evaluatethe movement of pesticides in several soils (Closeet al. 1999, 2003; Sarmah et al. 2005, 2006; Dannet al. 2006). GLEAMS has been employed in thedevelopment of NPLAS (EBoP 2007).

LEACHM (Leaching EstimationAnd CHemistry Model)

LEACHM comprises several modules of a process-based simulation model designed to describe thesoil water regime and chemistry and the transportof solutes in unsaturated or partially saturated soils(Hutson 2003). It is primarily a research model, usedfor simulating water and chemical transport downinto the soil profile (one dimension, up to 2 m).There are several versions of the model, each sharingcommon descriptions of the water balance and solutemovement but with different "chemistry" modules.The LEACHN version simulates N dynamics.LEACHM simulates plant growth at a very simplelevel only to obtain estimates of water and soluteuptake. Simulations of plant growth with varyingsystem management has been done coupled withother models (Mohtar et al. 1997). GIS integrationhas also been developed by third party researchers(Macur et al. 2000). LEACHM has been used in awide variety of studies worldwide. In New Zealand,it has been recently used for estimating nitrateleaching at an effluent treatment site (Mahmood et

al. 2002) and in a series of nationwide studies onpesticide leaching (Close et al. 1999,2003; Sarmahet al. 2005,2006; Dann et al. 2006).

HYDRUS

HYDRUS is a software package developed tosimulate the movement of water, heat, and multiplesolutes in variably saturated media (Simunek etal. 2005). The flow media can be composed ofnon-uniform soils, with dual porosity formulationpossible in later versions. Two different distributionsare available, HYDRUS-1D for one-dimensionalsimulations, which is a stand-alone freeware package(HYDRUS 2008), and HYDRUS (formerly knownas HYDRUS-2D), which allows simulations in twoor three dimensions. The model uses numericaltechniques to solve mechanistic water and solutetransport equations. The flow can occur in the vertical,horizontal, or any given inclined direction. The soillayering can be arbitrary and the monitoring depth(s)can be specified. HYDRUS does not simulate plantgrowth or system management; only a sink termto account for plant water uptake is available. Twointeresting features of HYDRUS are its ability tomodel solute transport with dual-domain porosityand capacity for simulations in 2 and 3-D. Thesefeatures maybe useful for assisting the developmentof simpler models that do not explicitly acknowledgethe presence of non-equilibrium flow processes.

The HYDRUS model is widely recognised andhas been used in numerous studies, chiefly in theUnited States and Europe. It has been used in NewZealand to simulate the water regime in lysimeters(Mertens et al. 2005), the nitrate and bacteriamovement towards groundwater (Pang et al. 2006),and in several studies on pesticide leaching (Closeet al. 1999, 2003; Pang et al. 2000; Sarmah et al.2005, 2006; Dann et al. 2006).

other modelsThere are a considerable number of models availablethat have not been considered in this review inorder to keep it within the proposed scope. Someare briefly mentioned here to highlight the rangeof applications for simulating nutrient losses fromfarmland. The main focus of this review is thedescription of nutrient losses via leaching, whichis a major cause of economic and environmentalconcern. Lately, however, there has been increasingefforts to improve the description of gaseous lossesfrom farming systems, especially N2O. These lossesare in general of low significance for computingthe N balance, but they can be highly important for

Cichota & Snow—Estimating nutrient loss from pastoral farms 249

issues such as climate change. Some of the modelsalready presented, such as EcoMod and APSIM,also produce estimates of gaseous losses, but thereare others built especially for this purpose. The mostdeveloped model for simulating gaseous losses inNew Zealand is probably the DNDC (DeNitrification-DeComposition) model (Li et al. 1992; Saggar et al.2004,2007b). This is a process-based model whichcan describe simultaneously emissions of trace-gases, soil carbon sequestration, and plant yield.Developed in the United States, the model has beenadapted to the New Zealand conditions and hasalready been used in some studies (Giltrap et al.2007; Saggar et al. 2007a). Other tools available formodelling gaseous losses include DayCent (Stehfest& Müller 2004), WNMM (Li et al. 2007b), andthe use of Automated Neural Networks (Ryan etal. 2004).

There are also several researchers in New Zealandwho have developed their own modelling tools,generally for some quite specific job or researchproject. Some of the approaches taken may besimilar to the models discussed above. Other models,however, have been built because of the lack offlexibility or some specific capability in the variousexisting tools. Some of these models may be ofinterest in the near feature, especially with regardto risk analysis. These include models built with aprobabilistic approach that may include mechanisticdescription of solute interactions in the soil andalso spatial variability due to non-uniform returnof nutrient via animal excreta (e.g., White et al.1998; Shorten & Pleasants 2007; Vogeler et al. 2007;Zhang & Tillman 2007; Wang 2008).

d I s c u s s I o n

There is a wide variety of models able to simulatenutrient balances in New Zealand pastoral farmsat various scales (Fig. 3). This variety may seemconfusing, but it reflects differing background ofmodellers and the different purposes for the models.Models can be used for a wide range of applications,from research purposes, to environmental or policyassessment, farm management, land-use riskassessment, and project design and evaluation.Thus, knowing their strengths and limitationsis important to select the appropriate tool andensure correct usage. It is important to note thatmodels are simplified descriptions of the naturalsystems and all models have shortcomings. Modelperformance is often limited because of incomplete

knowledge of the system or processes and due toassumptions, explicit and implicit, made by themodeller (Beven 2002; Hojberg & Refsgaard 2005;Council for Regulatory Environmental Modeling2008). Therefore, uncertainties always exist in allmodelling efforts.

While the issue of how to deal with modeluncertainty is under active development, it is oftenneglected by developers and users (Pappenberger& Beven 2006; Refsgaard et al. 2006; Brown &Heuvelink 2007; Lowell 2007). The system beingdescribed can be itself highly variable. Also,measurement procedures to obtain the data usedin the model development and validation are aninevitable source of uncertainty. For example, it iscommon to compare simulated N leaching againstmeasurements, but all measurements of leachingat any relevant scale are exceedingly variablewhenever the experimental methodology hasincluded replication (Addiscott 1996; Refsgaardet al. 1999; Pakrou & Dillon 2004). Therefore,large uncertainties associated with the output areunavoidable when modelling such process.

The variability of many environmental processesis related to the spatio-temporal scale at which theyare described. For example, long-term averages oflarge areas tend to present considerably less variationthan a single point on a daily basis. The intuitive stepof increasing the level of detail of a model seeking fora reduction of the prediction uncertainties may notyield the expected result. Increasing the model detailcan also make gathering the appropriate parametersmore difficult, and may as a result introduce moreuncertainty into the model estimates. The use ofunreliable parameters and input data is regardedas the major source of uncertainty in modelling(Addiscott & Tuck 2001; de Vries et al. 2003; Schoups& Hopmans 2006). Well calibrated process-orientedmodels are expected to present small uncertainties atsmall scales. However, the high specificity of thesemodels and the large variability of the processesmodelled tend to amplify the uncertainties whenincreasing the scale (Addiscott 1995; Schlecht& Hiernaux 2005; Sarmah et al. 2006). Farm orcatchment budgeting models average out most of thevariability by targeting the predictions to large areasand time spans and so allowing simpler descriptionsof many processes (Kersebaum & Wenkel 1998;Schlecht & Hiernaux 2005).

Models, thus, should not be regarded as corrector incorrect because of the uncertainty level. It ismore appropriate to infer applicable models for agiven set of requisites. That means selecting a model

250 New Zealand Journal of Agricultural Research, 2009, Vol. 52

JOUL >PwH«k

Fig. 3 Distribution of modelsused for estimating nutrient losssorted according to temporal andspatial scale.

HTTDHUE

LEACMM

designed to handle the relevant processes and thepossible variations on the management practicesat the scale of interest (Rykiel 1996; Scoones &Toulmin 1998; Schlecht & Hiernaux 2005; Cichota& Snow 2008). A compromise might have to bemade when choosing or developing a model in orderto consider the majority of processes occurring inthe system with limited input data. This may implyaccepting a higher level of uncertainty.

Nutrient budget models are appealing because oftheir requirement of a relatively modest amount ofinput data (Watson & Atkinson 1999; Öborn et al.2003). However, specific conditions or situationsof interest which deviate from the norm or theaverage might be missed. Models with higher levelof detail, such as process-oriented and mechanisticmodels, may be better suited for such tasksbecause they can often be set to describe systemswith greater specificity. This seems to generallyincrease the confidence in the model simulations,even though specificity does not necessarily meangreater accuracy. The amount of data required toset up simulations using process-oriented models

is a significant limitation of their usage. Also, somedegree of expertise is recommended for interpretingthe results from these models (Pappenberger &Beven 2006; Lowell 2007).

Models are often better at describing relativedifferences, such as the increase or reduction of Nleaching after a management change, rather thanproviding the absolute values of leaching. It isimportant to take this into account when analysingmodel outputs. Dynamic paddock models andprocess-oriented soil models can also be usefulto derive simpler functions for improving orcalibrating the coarser-scale catchment or farmbudgeting models. This is an approach which isgaining popularity (Young et al. 1996; Khaither &Erechtchoukova 2007). For instance, upgrades forthe crop and horticultural modules of OVERSEER®are being supported by simulations made usingprocess-oriented models, including SPASMO andLUCI (Whiteman & Brown 2009).

In New Zealand, nutrient budgets havebeen successfully used for supporting fertilisermanagement for quite a long time and recently have

Cichota & Snow—Estimating nutrient loss from pastoral farms 251

been used as monitoring tools for environmentalpolicy (Ledgard et al. 2004; Dragten & Thorrold2005; Wheeler et al. 2006; Monaghan et al. 2007).The ability of such models to evaluate managementoptions is quite limited, but increase as models suchas OVERSEER® are updated (Wheeler et al. 2006;Wheeler 2009). Process oriented models are alsogaining popularity and their use is likely to increase.They have been applied in several research studiesand more recently have been used to identify causesfor nutrient loss and mitigation strategies at paddocklevel (Mahmood et al. 2002; Lilburne et al. 2003;Rosen et al. 2005; Bryant et al. 2007; Green et al.2007). Likewise, sources and pathways for nutrientdischarge into large water bodies and the groundwaterhave been assessed using modelling (Alexander et al.2002b; Elliott et al. 2005; Rutherford 2005; Bidwell& Good 2007). Models, therefore, have shown theirusefulness and certainly have a key role to play inthe assessment and monitoring of nutrient losses.Their use, when made with discernment, will surelybe very constructive for improving farming andenvironmental standards in New Zealand.

long-term average type modelsOVERSEER® is the only user-friendly model alreadyavailable and in use in New Zealand with sufficienttrained consulting staff prepared to deliver farm-level nutrient budgeting. The model is an appropriatetool for the estimation of N and P balances at farm/paddock level. It uses easily accessible input data andaccounts for most of the various farm managementpractices typical in New Zealand. The OVERSEER®model has shown its usefulness as a decision supporttool for fertiliser management, although a morecomprehensive and publicly available documentationand some more validation tests could be beneficial.The scientific credibility of OVERSEER® wouldbe enhanced if more information about the modelwas available (Brown & Bewsell 2008) and suchdocumentation is currently in preparation (D. M.Wheeler, 2008 pers. comm.). Documentation for theother long-term average type model, NPLAS, is alsonot widely available, because NPLAS is only in adevelopment and testing phase.

The proposal to use OVERSEER® as a toolto support policy decisions and environmentalmonitoring seems feasible, as such an approachhas already been implemented in other countries(Goodlass et al. 2003; Oenema et al. 2003). Howeverthe limitations of a budget model for such a taskshould be acknowledged. Nutrient budgets arevery useful for providing a snapshot of the current

situation and to start informed debate on actions andpolicies for tackling environmental issues (Scoones& Toulmin 1998; Goodlass et al. 2003; Gourleyet al. 2007). These budgets have lesser ability todistinguish the effects of natural variability and itsinteractions with management practices, especiallyat non-steady state and/or non-uniform conditions.Because of this, predictions of future trends resultingfrom land-use change or climate variation should bemade with care (Öborn et al. 2003; Oenema et al.2003).

dynamic paddock and farm system modelsOf the models in this category, SPASMO has beentested the most under New Zealand conditions andhas been used in the widest variety of situationsand locations nationwide. It lacks, however, a gooduser interface and the documentation about it is alsoscattered. APSIM has had fewer applications in NewZealand, but has been extensively used in Australiaand overseas. The documentation is freely availableand it has a comprehensive user interface. The othermodels in this category are still being developed and/or need more validation tests. It should be noted thatEcoMod has the most user friendly interface and alsois very well documented.

There are clear differences between these modelswith respect to their background and flexibility.SPASMO has been used mostly for horticulture,EcoMod for pastoral systems, and LUCI andAPSIM have primarily been used for croppingsystems. This may be relevant when choosing onemodel, although the basic processes are describedsimilarly in all of them. Most important perhaps isthe flexibility for setting up simulations. SPASMOhas been used for quite a variety of systems, but itcannot dynamically simulate a whole farm, whichis important for pastoral systems. User expertiseis required to integrate several individual paddocksimulations into a farm-average value. SPASMOalso assumes a uniform return of animal excretato the paddock, ignoring a process that is knownto be very important in the leaching process fromgrazed paddocks (Ball & Ryden 1984; Haynes &Williams 1993; Di et al. 2002; Pleasants et al. 2007).APSIM, being a modular framework, is the tool thatoffers the most flexibility; there are a great numberof modules already available that can be selectedand integrated by a powerful management engine(Keating et al. 2003; Holzworth et al. in press). Also,with the implementation of the Common ModellingProtocol (Moore et al. 2007), the development of newmodules is open to a broader number of researchers,

252 New Zealand Journal of Agricultural Research, 2009, Vol. 52

and their implementation is relatively easy. Forinstance, the ability of APSIM to simulate pastoralsystems has been strengthened by the inclusion of theFarmWi$e (Moore 2001) and EcoMod modules intoits modelling framework. A promising future modelis the combination of the well-tested and validatedsoil and nutrient modules in APSIM connected to theequally well-tested pasture modules in EcoMod.

catchment modelsMost of the models presented in this category are ofinterest for research and management at catchmentscale. Users of these models or their results includeregional councils and environmental agencies.Although these models are not limited to catchmentscales, they have their strength or have been mostlyused at such scale.

For land planning and as a policy-making supporttool, the CLUES framework and AquiferSim havevery attractive features. CLUES is still relativelysimple and is able to integrate land use, managementpractices, and system transfers within a catchmentarea and beyond. It also provides a summary ofresults in the form of maps. AquiferSim also is ona similar stand, but while CLUES concentrateson surface water processes, AquiferSim's strengthis in simulating groundwater transport. There isclear complementarity between these models, anddiscussions for cooperation are in progress (L.Lilburne, 2009 pers. comm.). However, both CLUESandAquiferSim are ongoing projects and need furtherdevelopment and testing. Other models from thiscategory, notably SPARROW and EnSus, also workat this level and may be useful for environmentalmanagement and policy-making support. ROTAN isunder development and, along with NLE, needs tobe calibrated to regions other than those they weredeveloped for. Most of these models, however, arenot detailed enough to respond to fine variations inthe landscape or to changes in farm management.

soil process modelsThese models, due to their higher level of complexity,seem to remain applicable mainly for scientific andconsulting purposes. Soil process models are veryuseful to investigate particular processes in the soilin detail and have a significant role in developingand testing simpler models. They can be used forevaluating non-equilibrium systems and also extremecase scenarios. They lack, however, flexibility toaccount for the integration of varying managementpractices. In the context of this review, these modelscan be useful for testing some of the assumptions

of coarser-scale models or for developing genericfunctions for use elsewhere. Specific complexsituations that demand a high level of descriptionof the soil profile, such as sharp impeding layers,or the interactions between soil-water flow andplant roots, are also issues to be tackled with thesecomplex models.

c o n c l u s I o n s

The application of models for nutrient managementis already a reality in New Zealand. Whether theestimation of nutrient loss is focused on preventingenvironmental impact or ensuring high levels offarm productivity, models have already shown theirusefulness. A new development gaining momentumis the use of models to establish N managementtargets, such as fertiliser caps, and to demonstratecompliance with legislation. This increasing usemakes it more important to recognise the differencesbetween models. Although overlaps do happen, ingeneral most of the models presented in this revieware best applicable to a limited set of problems andat differing scales. Knowing their strengths anddeficiencies will help to select models properly andunderstand better their results.

There is much misunderstanding and lack ofinformation about the potential of existing tools andthat may be limiting their use and acceptance. Werecommend that model developers disclose moreinformation about the assumptions, purpose andknown uncertainties of their models. It is importantto maintain the development and testing of thesemodels, as well as the studies on the actual processesinvolved on nutrient loss, so that the descriptionscan be better understood and improved. How wecommunicate and deal with uncertainty, whetherarising from measurements or from modelling,is a challenge that deserves a close look in thenear future. With a discerning approach and morereliable outputs we will be able to reach wiserdecisions.

A c K n o w l e d g M e n t s

We thank the New Zealand-based model developersfor valuable discussion during this work. This workwas conducted under the Pasture Research ProgrammeEnvironment (C10X0603) which is jointly funded bythe Foundation for Research, Science and Technology,DairyNZ, Fonterra, and Meat and Wool NZ.

Cichota & Snow—Estimating nutrient loss from pastoral farms 253

REFERENCES

Addiscott TM 1995. Modelling the fate of crop nutrientsin the environment: problems of scale andcomplexity. European Journal of Agronomy4(4): 413-417.

Addiscott TM 1996. Measuring and modelling nitrogenleaching: Parallel problems. Plant and Soil181(1): 1-6.

Addiscott TM, Whitmore AP 1991. Simulation of soluteleaching in soils of differing permeabilities. SoilUse and Management 7(2): 94-102.

Addiscott TM, Tuck G 2001. Non-linearity and error inmodelling soil processes. European Journal ofSoil Science 52(1): 129-138.

AgResearch 2007. OVERSEER® nutrient budgets.www.agresearch.co.nz/overseerweb/default.aspx(accessed June 2007).

Alexander RB, Elliott AH, Shankar U, McBride GB2002a. Estimating the sources and transportof nutrients in the Waikato River Basin, NewZealand. Water Resources Research 38(12): 23.

Alexander RB, Johnes PJ, Boyer EW, Smith RA 2002b.A comparison of models for estimating theriverine export of nitrogen from large watersheds.Biogeochemistry 57(1): 295-339.

APSRU 2008. APSIM—Agricultural production systemssimulator, www.apsim.info

Asseng S, Jamieson PD, Kimball B, Pinter P, Sayre K,Bowden JW, Howden SM 2004. Simulated wheatgrowth affected by rising temperature, increasedwater deficit and elevated atmospheric CO2. FieldCrops Research 85(2/3): 85-102.

Ball PR, Ryden JC 1984. Nitrogen relationships inintensively managed temperate grasslands. Plantand Soil 76(1-3): 23-33.

Beven K 1989. Changing ideas in hydrology—the case ofphysically-based models. Journal of Hydrology105(1-2): 157-172.

Beven K 2002. Towards a coherent philosophy formodelling the environment. Proceedings of theRoyal Society of London Series A—MathematicalPhysical and Engineering Sciences 458(2026):2465-2484.

Bidwell VJ, Good JM 2007. Development of theAquiferSim model of cumulative effecton groundwater of nitrate discharge fromheterogeneous land use over large regions.In: Kulasiri D, Oxley L ed. MODSIM2007—International Congress on Modeling andSimulation, December 2007, Christchurch, NewZealand. Pp. 1617-1622.

Bidwell VJ, Lilburne LR, Good JM 2005. Strategy fordeveloping GIS-based tools for management ofthe effects on groundwater of nitrate leachingfrom agricultural land use. In: Zerger A, ArgentRM ed. MODSIM 2005—International Congresson Modeling and Simulation, December 2005,Melbourne, Australia. Pp. 1354-1360.

Boote KJ, Jones JW, Pickering NB 1996. Potential usesand limitations of crop models. Agronomy Journal88(5): 704-716.

Brown JD, Heuvelink GBM 2007. The data uncertaintyengine (DUE): A software tool for assessing andsimulating uncertain environmental variables.Computers & Geosciences 33(2): 172-190.

Brown M, Bewsell D 2007. Nutrient budgeting—reporton farmer interviews. AgResearch Internal Report,11 p.

Brown M, Bewsell D 2008. Nutrient models—reporton researcher interviews. AgResearch InternalReport. 13 p.

Bryant JR, Hoogendoorn CJ, Snow VO 2007. Simulationof mitigation strategies to reduce nitrogen leachingfrom grazed pasture. Proceedings of the NewZealand Grassland Association 69: 145-151.

Bryant JR, Snow VO, Cichota R 2008. Investigating theeffect of spatial variability in climate, soil type,animal type and nitrogen use on environmentaloutcomes of grassland farming systems aroundLake Taupo. AgResearch Internal Report. 22 p.

Bywater AC, Cacho OJ 1994. Use of simulation models inresearch. Proceedings of the New Zealand Societyof Animal Production 54: 403-412.

Cichota R, Snow VO 2008. The role of three differentmodels for estimating nutrient loss from pastoralfarms. In: Currie LD, Yates LJ ed. Carbon andnutrient management in agriculture. OccasionalReport No. 21. Palmerston North, NZ, Fertiliserand Lime Research Centre, Massey University.Pp. 222-232.

Close ME, Watt JPC, Vincent KW 1999. Simulation ofpicloram, atrazine, and simazine transport throughtwo New Zealand soils using LEACHM. AustralianJournal of Soil Research 37(1): 53-74.

Close ME, Pang L, Magesan GN, Lee R, Green SR 2003.Field study of pesticide leaching in an allophanicsoil in New Zealand. 2: Comparison of simulationsfrom four leaching models. Australian Journal ofSoil Research 41(5): 825-846.

Close ME, Sarmah AK, Flintoft MJ, Thomas J, HughesB 2006. Field and laboratory study of pesticideleaching in a Motupiko silt loam (Nelson) and in aWaikiwi silt loam (Southland). Australian Journalof Soil Research 44(6): 569-580.

254 New Zealand Journal of Agricultural Research, 2009, Vol. 52

Condron LM, Cameron KC, Di HJ, CloughTJ, Forbes EA,McLaren RG, Silva RG 2000. A comparison ofsoil and environmental quality under organic andconventional farming systems in New Zealand.New Zealand Journal of Agricultural Research43(4): 443-466.

Council for Regulatory Environmental Modeling 2008.Guidance on the development, evaluation andapplication of environmental models (Draft). USEnvironmental Protection Agency. 95 p.

Cullen BR, Eckard RJ, Callow MN, Johnson IR, ChapmanDF, Rawnsley RP, Garcia SC, White T, SnowVO 2008. Simulating pasture growth rates inAustralian and New Zealand grazing systems.Australian Journal of Agricultural Research59(8): 761-768.

Dairy Environment Review Group 2006. Dairyindustry strategy for sustainable environmentalmanagement. Dairy Insight. 24 p.

Dairy Insight 2006. Dairying for the environment. 8 p.

Dann RL, Close ME, Lee R, Pang L 2006. Impact ofdata quality and model complexity on predictionof pesticide leaching. Journal of EnvironmentalQuality 35(2): 628-640.

de Vries W, Kros J, Oenema O, de Klein J 2003.Uncertainties in the fate of nitrogen II: aquantitative assessment of the uncertainties inmajor nitrogen fluxes in the Netherlands. NutrientCycling in Agroecosystems 66(1): 71-102.

Di HJ, Cameron KC 2000. Calculating nitrogen leachinglosses and critical nitrogen application rates indairy pasture systems using a semi-empiricalmodel. New Zealand Journal of AgriculturalResearch 43(1): 139-147.

Di HJ, Cameron KC, Silva RG, Russell JM, BarnettJW 2002. A lysimeter study of the fate of N-15-labelled nitrogen in cow urine with or withoutfarm dairy effluent in a grazed dairy pasture soilunder flood irrigation. New Zealand Journal ofAgricultural Research 45(4): 235-244.

Di HJ, Cameron KC, Bidwell VJ, Morgan MJ, HansonC 2005. Apilot regional scale model of land useimpacts on groundwater quality. Management ofEnvironmental Quality 16(3): 220-234.

Dragten R, Thorrold BS 2005. Consents for farming:regulating nutrient loss in the lake Taupocatchment. Developments in fertiliser applicationtechnologies and nutrient management.Occasional Report No. 18. Palmerston North,NZ, Fertilizer and Lime Research Centre, MasseyUniversity.

EBoP 2007. Nitrogen phosphorus load assessmentsystem. www.envbop.govt.nz/ (accessed 20May 2007).

Eckersten H, Jansson PE 1991. Modelling water flow,nitrogen uptake and production for wheat. NutrientCycling in Agroecosystems 27(2): 313-329.

Elliott AE, Alexander RB, Schwarz GE, Shankar U, SukiasJPS, McBride GB 2005. Estimation of nutrientsources and transport for New Zealand using thehybrid mechanistic-statistical model SPARROW.New Zealand Journal of Hydrology 44: 1-27.

FLRC 2008. Sustainable nutrient nanagement programme,flrc.masseyac.nz (accessed July 2009).

Giltrap D, Saggar S, Li C, Wilde H 2007. Using theNZ-DNDC model to estimate agricultural N2Oemissions in the Manawaru-Wanganui region.Plant and Soil: 1-19.

Goodlass G, Halberg N, Verschuur G 2003. Input outputaccounting systems in the European community—an appraisal of their usefulness in raisingawareness of environmental problems. EuropeanJournal of Agronomy 20(1-2): 17-24.

Gourley CJP, Powell JM, Dougherty WJ, Weaver DM2007. Nutrient budgeting as an approach toimproving nutrient management on Australiandairy farms. Australian Journal of ExperimentalAgriculture 47(9): 1064-1074.

Green S, Vogeler I, Deurer M, Clothier BE 2000. Modellingthe impact of irrigation on nitrate leaching froma dairy farm. HortResearch. HortResearch ClientReport No. 2001/189.

Green SR, Snow V, Clothier BE 2003a. Modelling thenitrogen dynamics under pasture irrigated withdairy effluent. HortResearch Client Report No.2003/6115.

Green SR, Vogeler I, Clothier BE, Mills TM, van denDijssel C 2003b. Modelling water uptake bya mature apple tree. Australian Journal of SoilResearch 41(3): 365-380.

Green S, Greven M, Neal S, Clothier BE 2004a. Anassessment of vineyard planting density andthe water demand of grapes. HortResearch.HortResearch Client Report No. 2004/12344.42 p.

Green SR, Greven M, Neal S, Clothier BE 2004b. Anassessment of vineyard planting density andthe water demand of grapes. HortResearch.HortResearch Client Report No. 2004/12344.42 p.

Green SR, Wilde H, Deurer M, Vogeler I, MagesanGN 2007. Modelling nitrogen, phosphorus andbacteria transport in soils. In: Wang H, QuinternMed. 2007 Annual Conference of the New ZealandLand Treatment Collective—Nutrient Removaland Water Quality Issues, 14-16 March 2007,Rotorua, New Zealand Pp. 15-23.

Cichota & Snow—Estimating nutrient loss from pastoral farms 255

Haynes RJ, Williams PH 1993. Nutrient cycling andsoil fertility in the grazed pasture ecosystem.Advances in Agronomy 49: 119-199.

Hewitt A, Stephens P 2002 EnSus: Integrated spatialrisk analysis framework for determining bestmanagement practices for environmentalsustainability. Soil Horizons: 9-10.

Hojberg AL, Refsgaard JC 2005. Model uncertainty—parameter uncertainty versus conceptual models.Water Science and Technology 52(6): 177—186.

Holzworth DP, Huth NI, de Voil PG in press. Simplifyingenvironmental model reuse. EnvironmentalModelling & Software.

Horizons Regional Council 2007. Farmer-appliedresource management strategy 127 p.

Hunt J, van Rees H, Hochman Z, Carberry P, HolzworthD, Dalgliesh N, Brennan L, Poulton P, van ReesS, Huth N and others 2006. Yield Prophet®:an online crop simulation service. In: FischerT, Turner N, Angus J, Mclntyre L, RobertsonM, Borrell A, Lloyd D ed. 13th AustralianAgronomy Conference, September 2006, Perth,WA, Australia.

Huth NI, Snow VO, Keating BA 2001. Integrating aforest modelling capability into an agriculturalproduction system modelling environment—current applications and future possibilities. In:Ghassemi F, Whetton P, Little R, Littleboy Med. MODSIM 2001—International Congresson Modelling and Simulation, December 2001,Australian National University, Canberra,Australia. Pp. 1895-1900.

Hutson JL 2003. Leaching estimation and chemistrymodel—model description and user's guide. TheFlinders University of South Australia. 142 p.

Hutson JL, Wagenet RJ 1993. A pragmatic field-scaleapproach for modeling pesticides. Journal ofEnvironmental Quality 22(3): 494-499.

HYDRUS 2008. HYDRUS-1D for Windows, www.pc-progress.cz/Pg_Hydrus_lD.htm (accessedJuly 2009).

IRAP 2008. Integrated research for aquifer protection.irap.org.nz (accessed July 2009).

Jamieson PD, Semenov MA 2000. Modelling nitrogenuptake and redistribution in wheat. Field CropsResearch 68(1): 21-29.

Jamieson PD, Semenov MA, Brooking IR, Francis GS1998. Sirius: a mechanistic model of wheatresponse to environmental variation. EuropeanJournal of Agronomy 8(3/4): 161-179.

Jamieson PD, Zyskowski RF, Sinton SM, Brown HE,Butler RC 2006a. Potato calculator: a tool forscheduling nitrogen fertiliser applications. 36thAnnual Conference of the Agronomy Societyof New Zealand, 14 November 2006, OtagoUniversity, Dunedin, New Zealand. Pp. 49-53.

Jamieson PD, Zyskowski RF, Li FY, Francis GS, Snow V,Lilburne L 2006b. LUCI in the sky with diamonds—modelling the wider impacts of land use changeand intensification. In: Fischer T, Turner N, AngusJ, McIntyre L, Robertson M, Borrell A, LloydD ed. 13th Australian Agronomy Conference,September 2006, Perth, WA, Australia.

Johnson IR, Lodge GM, White RE 2003. The sustainablegrazing systems pasture model: description,philosophy and application to the SGS nationalexperiment. Australian Journal of ExperimentalAgriculture 43(7-8): 711-728.

Johnson IR, Parsons A, Eckard RJ, Clark H, ChapmanDF, Newton P 2004. EcoMod: a biophysicalagricultural model with a focus on CO2, N2O andCH4 emissions. In: Eckard R, Slattery B ed. 2ndJoint Australia and New Zealand Forum on Non-CO2 Greenhouse Gas Emissions from Agriculture,October 2003, Canberra, Australia. Pp. B1.

Johnson IR, Chapman DF, Snow VO, Eckard RJ, ParsonsAJ, Lambert MG, Cullen BR 2008. DairyMod andEcoMod: biophysical pasture-simulation modelsfor Australia and New Zealand. Australian Journalof Experimental Agriculture 48: 621-631.

Keating BA, Carberry PS, Hammer GL, Probert ME,Robertson MJ, Holzworth D, Huth NI, HargreavesJNG, Meinke H, Hochman Z and others 2003.An overview of APSIM, a model designed forfarming systems simulation. European Journal ofAgronomy 18(3-4): 267-288.

Kersebaum KC, Wenkel KO 1998. Modelling waterand nitrogen dynamics at three different spatialscales—influence of different data aggregationlevels on simulation results. Nutrient Cycling inAgroecosystems 50(1-3): 313-319.

Khaither PA, Erechtchoukova MG 2007. From complexto simple in environmental simulation modelling.In: Oxley L, Kulasiri D ed. MODSIM 2007—International Congress on Modelling andSimulation, New Zealand, December 2007,University of Canterbury, Christchurch. Pp.2069-2075.

Knisel WG, Davis FM 2000. GLEAMS—Groundwaterloading effects of agricultural managementsystems—user manual. 191 p.

Kutra G, Aksomaitiene R 2003. Use of nutrientbalances for environmental impact calculationson experimental field scale. European Journal ofAgronomy 20(1-2): 127-135.

255 New Zealand Journal of Agricultural Research, 2009, Vol. 52

Larcombe MT 1999. UDDER for Windows: a desktopdairyfarm for extension and research—operatingmanual. Maffra Herd Improvement Co-op.

Ledgard SF, Power I 2006. Nitrogen and phosphorus lossfrom "average" Waikato farms to waterways asaffected by best or potential managementpractices. AgResearch Ltd. 11 p.

Ledgard SF, Williams PH, Broom FD, Thorrold BS,Wheeler DM, Willis VJ 1999. Overseer™—Anutrient budgeting model for pastoral farming,wheat, potatoes, apples and kiwifruit. In: CurrieLD, Hedley MJ, Home DJ, Loganathan P ed.Best soil management practices for production.Occasional Report No. 12. Palmerston North,NZ, Fertiliser and Lime Research Centre, MasseyUniversity. Pp. 143-152.

Ledgard SF, Steel K, Roberts AHC, Journeaux PR 2000.Use of OVERSEER™ to compare farm systemsand countries for nutrient balances, losses andefficiency In: Currie LD, Loganathan P ed. SoilResearch—a knowledge industry for land-basedexporters. Occasional Report No. 13. PalmerstonNorth, New Zealand. Fertilizer and LimeResearch Centre, Massey University. Pp. 83 -91.

Ledgard SF, Thorrold BS, Petch RA, Young J 2001. Useof OVERSEER as a tool to identify managementstrategies for reducing nitrate leaching fromfarms around Lake Taupo. In: Currie LD,Loganathan P ed. Precision tools for improvingland management. Occasional Report No. 14.Fertilizer and Lime Research Centre, PalmerstonNorth, New Zealand. Pp. 187-194.

Ledgard SF, Journeaux PR, Furness H, Petch RA,Wheeler D 2004. Use of nutrient budgeting andmanagement options for increasing nutrient useefficiency and reducing environmental emissionsfrom New Zealand farms. OECD expert meetingon farm management indicators and theenvironment, 8-12 March 2004, PalmerstonNorth, New Zealand.

Leonard RA, Knisel WG, Still DA 1987. GLEAMS:groundwater loading effects of agriculturalmanagement systems. Transactions of the ASAE30(5): 1403-1418.

Li C, Frolking S, Frolking TA 1992. A model of nitrousoxide evolution from soil driven by rainfallevents: 1. Model structure and sensitivity.Journal of Geophysical Research 97(D9):9759-9776.

Li FY, Jamieson PD, Pearson AJ 2006. AmaizeN:developing a decision-support tool to optimisenitrogen management of maize. In: KempPDSVS ed. Agronomy New Zealand, OtagoUniversity, Dunedin, New Zealand. Pp. 61-70.

Li FY, Jamieson PD, Zyskowski RF, Brown HE,Pearson AJ 2007a. The crop calculators—fromsimulation models to usable decision-supporttools. In: Kulasiri D, Oxley L ed. MODSIM2007—International Congress on Modelling andSimulation, December 2007, Christchurch, NewZealand. Pp. 128-134.

Li Y, White R, Chen D, Zhang J, Li B, Zhang Y, HuangY, Edis R 2007b. A spatially referenced waterand nitrogen management model (WNMM) for(irrigated) intensive cropping systems in theNorth China Plain. Ecological Modelling 203(3-4): 395-423.

Lilburne LR, Webb TH 2002. Effect of soil variability,within and between soil taxonomic units, onsimulated nitrate leaching under arable farming,New Zealand. Australian Journal of Soil Research40(7): 1187-1199.

Lilburne LR, Webb TH, Francis GS 2003. Relative effectof climate, soil, and management on risk ofnitrateleaching under wheat production in Canterbury,New Zealand. Australian Journal of Soil Research41(4): 699-709.

Lilburne LR, Bright J, Francis G, Close M, Bidwell V,Snow V, Thorrold B, Crisley K, Smith V 2006.The IRAP project: predictive farm- and regional-scale tools for assessing land use impacts ongroundwater quality. In: Currie LD, Hanly JA ed.Implementing sustainable nutrient managementstrategies in agriculture. Occasional Report No.19. Palmerston North, New Zealand, Fertilizerand Lime Research Centre, Massey University.Pp. 440-444.

Lilburne L, Bright J, Thomas S, Bidwell V, Close M,Crisley K, Dalley D, Snow VO 2008. IRAP:integrated research for aquifer protection. Soils2008—Australian and New Zealand 4th Joint SoilsConference, 1-5 December, Palmerston North,New Zealand.

Lin HS, Wheeler D, Bell J, Wilding L 2005. Assessmentof soil spatial variability at multiple scales.Ecological Modelling 182(3-4): 271-290.

Livestock Improvement 2006. Dairy statistics 2005-2006.Livestock Improvement Corporation Limited.48 p.

Lowell KE 2007. At what level will decison-makers beable to use uncertainty information? In: KulasiriD, Oxley L ed. MODSIM 2007—InternationalCongress on Modelling and Simulation, December2007, Canterbury University, Christchurch, NewZealand. Pp. 2666-2672.

Macur RE, Gaber HM, Wraith JM, Inskeep WP 2000.Predicting solute transport using mapping-unitdata: model simulations versus observed data atfour field sites. Journal of Environmental Quality29(6): 1939-1946.

Cichota & Snow—Estimating nutrient loss from pastoral farms 257

Mahmood B, Russell JM, Wall GL 2002. Field-scale nitratesimulation. Transactions of the American Societyof Agricultural Engineers 45(6): 1835-1842.

Marshall PR, McCall DG, Johns KL 1991. Stockpol:a decision support model for livestock farms.Proceedings of the New Zealand GrasslandAssociation 53: 137-140.

Mertens J, Barkle GF, Stenger R 2005. Numericalanalysis to investigate the effects of the design andinstallation of equilibrium tension plate lysimeterson leachate volume. Vadose Zone Journal 4(3):488-499.

Ministry for the Environment 2006. New Zealand'sGreenhouse Gas Inventory 1990-2004. Ministryfor the Environment. 188 p.

Ministry of Agriculture and Forestry 2007. Situation andoutlook for New Zealand agriculture and forestry(August 2007). MAF Policy. 73 p.

Mohtar RH, Jabro JD, Buckmaster DR 1997. A grazingsimulation model: GRASIM B: Field testing.Transactions of the ASAE 40(5): 1495-1500.

Monaghan RM, Hedley MJ, Di HJ, McDowell RW,Cameron KC, Ledgard SF 2007. Nutrientmanagement in New Zealand pastures—recentdevelopments and future issues. New ZealandJournal of Agricultural Research 50: 181-201.

Moore AD 2001. FarmWi$e: a flexible decisionsupport tool for grazing systems management.Proceedings of the Nineteenth InternationalGrassland Congress: 1045-1046.

Moore AD, Holzworth DP, Herrmann NI, Huth NI,Robertson MJ 2007. The common modellingprotocol: a hierarchical framework for simulationof agricultural and environmental systems.Agricultural Systems 95(1-3): 37-48.

O'Connor MB, Boddy IK, Morton JD, Edmeades DC1996. Nutrient budgets—a key indicator of soilsustainability. Proceedings of the New ZealandGrassland Association 58: 219-222.

Oborn I, Edwards AC, Witter E, Oenema O, IvarssonK, Withers PJA, Nilsson SI, Stinzing AR 2003.Element balances as a tool for sustainable nutrientmanagement: a critical appraisal of their merits andlimitations within an agronomic and environmentalcontext. European Journal of Agronomy 20(1-2):211-225.

Oenema O, Kros H, De Vries W 2003. Approaches anduncertainties in nutrient budgets: implications fornutrient management and environmental policies.European Journal of Agronomy 20(1-2): 3-16.

Pakrou N, Dillon PJ 2004. Leaching losses of N undergrazed irrigated and non-irrigated pastures. Journalof Agricultural Science 142(5): 503-516.

Pang L, Close ME, Watt JPC, Vincent KW 2000.Simulation of picloram, atrazine, and simazineleaching through two New Zealand soils andinto groundwater using HYDRUS-2D. Journal ofContaminant Hydrology 44(1): 19-46.

Pang L, Nokes C, Simunek J, Kikkert H, Hector R 2006.Modeling the impact of clustered septic tanksystems on groundwater quality. Vadose ZoneJournal 5(2): 599-609.

Pappenberger F, Beven KJ 2006. Ignorance is bliss: orseven reasons not to use uncertainty analysis.Water Resources Research 42 W05302 doi:10.1029/2005WROO4820.

Parliamentary Commissioner for the Environment2004. Growing for good: intensive farming,sustainability and New Zealand's environment.Parliamentary Commissioner for the Environment.236 p.

Pleasants AB, Shorten PR, Wake GC 2007. Thedistribution of urine deposited on a pasture fromgrazing animals. Journal of Agricultural Science145(1): 81-86.

Refsgaard JC, Thorsen M, Jensen JB, KleeschulteS, Hansen S 1999. Large scale modelling ofgroundwater contamination from nitrate leaching.Journal of Hydrology 221(3-4): 117-140.

Refsgaard JC, van der Sluijs JP, Brown J, van der Keur P2006. A framework for dealing with uncertaintydue to model structure error. Advances in WaterResources 29(11): 1586-1597.

Rosen MR, Reeves RR, Green S, Clothier B, IronsideN 2005. Prediction of groundwater nitratecontamination after closure of an unlined sheepfeedlot. Vadose Zone Journal 3(3): 990-1006.

Rutherford JC 2005. Modelling the effects of groundwaterlags on nitrate inputs to lakes Rotorua & Taupo,New Zealand. In: Zerger A, Argent RM ed.MODSIM 2005—International Congress onModelling and Simulation, December 2005,Melbourne, Australia. Pp. 2749-2754.

Rutherford JC, Spigel RH, Howard-Williams C, ElliottAE, Morgenstern U 2006. Improved tools formodelling nutrient runoff from agriculturalland and its effects on water quality. Water2006—International Water Conference, 1-4August 2006, Auckland, New Zealand.

Ryan M, Muller C, Di HJ, Cameron KC 2004. The use ofartificial neural networks (ANNs) to simulate N2Oemissions from a temperate grassland ecosystem.Ecological Modelling 175(2): 189-194.

Rykiel EJ 1996. Testing ecological models: the meaningof validation. Ecological Modelling 90(3):229-244.

New Zealand Journal of Agricultural Research, 2009, Vol. 52

Saggar S, Andrew RM, Tate KR, Hedley CB, RoddaNJ, Townsend JA 2004. Modelling nitrous oxideemissions from dairy-grazed pastures. NutrientCycling in Agroecosystems 68(3): 243-255.

Saggar S, Hedley CB, Giltrap DL, Lambie SM 2007a.Measured and modelled estimates of nitrousoxide emission and methane consumption from asheep-grazed pasture. Agriculture, Ecosystems &Environment 122(3): 357-365.

Saggar S, Giltrap DL, Li C, Tate KR 2007b. Modellingnitrous oxide emissions from grazed grasslandsin New Zealand. Agriculture, Ecosystems &Environment 119(1-2): 205-216.

Sarmah AK, Close ME, Pang LP, Lee R, Green SR 2005.Field study of pesticide leaching in a Himatangisand (Manawatu) and a Kiripaka bouldery clayloam (Northland). 2. Simulation using LEACHM,HYDRUS-1D, GLEAMS, and SPASMO models.Australian Journal of Soil Research 43(4):471-489.

Sarmah AK, Close ME, Dann R, Pang L, Green SR 2006.Parameter estimation through inverse modellingand comparison of four leaching models usingexperimental data from two contrasting pesticidefield trials in New Zealand. Australian Journal ofSoil Research 44(6): 581-597.

Schlecht E, Hiernaux P 2005. Beyond adding up inputsand outputs: process assessment and upscalingin modelling nutrient flows. Nutrient Cycling inAgroecosystems 70(3): 303-319.

Schoups G, Hopmans JW 2006. Evaluation of modelcomplexity and input uncertainty of field-scalewater flow and salt transport. Vadose Zone Journal5(3): 951-962.

Schwarz GE, Hoos AB, Alexander RB, Smith RA 2006. TheSPARROW surface water-quality model: theory,application, and user documentation. 122 p.

Scoones I, Toulmin C 1998. Soil nutrient balances: whatuse for policy? Agriculture, Ecosystems andEnvironment 71(1-3): 255-267.

Semadeni-Davies A, Shankar U, Elliott S 2006. TheCLUES project: tutorial manual for CLUES 1.4.NIWA Client report for the Ministry of Agricultureand Forestry, project MAF07205. 109 p.

Shorten PR, Pleasants AB 2007. A stochastic model ofurinary nitrogen and water flow in grasslandsoil in New Zealand. Agriculture, Ecosystems &Environment 120(2-4): 145-152.

Simunek J, van Genuchten MT, Sejna M 2005. TheHYDRUS-1D software package for simulatingthe one-dimensional movement of water, heat,and multiple solutes in variably-saturated media—Version 3.0. Department of EnvironmentalSciences, University of California Riverside.270 p.

Snow V, Green S, Gurnsey S, Veale C, Clothier B,Ironside N, Ensor P 2004. The GROWSAFE®

calculator: choosing pesticides that leach lessand do not build up in the soil. 3rd AustralianNew Zealand Soils Conference—Supersoil2004, 5-9 December 2004, Sydney, Australia.

Snow VO, Houlbrooke DJ, Huth NI 2007a. Predictingsoil water, tile drainage, and runoff in a mole-tile drained soil. New Zealand Journal ofAgricultural Research 50(1): 13-24.

Snow VO, Zyskowski RF, Cichota R, Lilburne LR,Jamieson PD 2007b. The effect of temporalaggregation on modelling pasture growth andnitrate leaching from urine patches. In: KulasiriD, Oxley L ed. MODSIM 2007—InternationalCongress on Modelling and Simulation,December 2007, University of Canterbury,Christchurch, New Zealand. Pp. 1646-1652.

Snow VO, Zyskowski RF, Martin RJ, Knight TL,Gillespie RN, Riddle MU, Fraser TJ, ThomasSM 2007c. Impact of irrigation variability onpasture production and beneficial water use.Proceedings of the New Zealand GrasslandAssociation 69: 59-64.

Snow VO, Johnson IR, Parsons AJ 2009a. A singleheterogeneous paddock approach to modellingthe effects of urine patches on production andleaching in grazed pastures. Crop and PastureScience 60: 691-696.

Snow VO, Lieffering M, Cullen BR, Johnson IR 2009b.Implications of heterogeneous urinary-N returnfor modelling pasture production and N lossesfrom grazed pastures under climate change.Integrated Assessment of Agriculture andSustainable Development Conference. Egmondaan Zee, The Netherlands, 10-12 March. Pp.2.

Statistics New Zealand 2006. Fertiliser use and theenvironment. 11 p.

Statistics New Zealand 2007. Agriculture in NewZealand. Analytical Reports. www.stats.govt.nz/analytical-reports/agriculrure-in-nz/default.htm (accessed 24 September 2007).

Stehfest E, Müller C 2004. Simulation of N2O emissionsfrom a urine-affected pasture in New Zealandwith the ecosystem model DayCent. Journalof Geophysical Research, D-Atmospheres109(3).

Stephens P, Sparling G, Hewitt A 2002 New system mapsout risks for land users. Discovery: 2.

Stephens PR, Hewitt AE, Sparling GP, Gibb RG,Shepherd TG 2003. Assessing sustainabilityof land management using a risk identificationmodel. Pedosphere 13(1): 41-48.

Cichota & Snow—Estimating nutrient loss from pastoral farms 259

Thomas SM, Ledgard SF, Francis GS 2005. Improvingestimates of nitrate leaching for quantifyingNew Zealand's indirect nitrous oxide emissions.Nutrient Cycling in Agroecosystems 73(2-3):213-226.

Treasury 2007. New Zealand economic and financialoverview. 56 p.

van Beek CL, Brouwer L, Oenema O 2003. The use offarmgate balances and soil surface balances asestimator for nitrogen leaching to surface water.Nutrient Cycling in Agroecosystems 67(3):233-244.

Vogeler I, Blard A, Bolan N 2007. Modelling DCD effecton nitrate leaching under controlled conditions.Australian Journal of Soil Research 45(4):310-317.

Vogeler I, Green S, Magesan G, Clothier B 2004.Measuring and modelling water dynamics undereffluent irrigated pine trees. Supersoil 2004:Proceedings of the 3rd Australian New ZealandSoils Conference, 5-9 December 2004, Sydney,Australia.

Wang FL 2008. Modeling the spatial distribution ofnitrogen leaching from dairy farmland. VadoseZone Journal 7(2): 439-452.

Watson CA, Atkinson D 1999. Using nitrogen budgetsto indicate nitrogen use efficiency and lossesfrom whole farm systems: a comparison of threemethodological approaches. Nutrient Cycling inAgroecosystems 53(3): 259-267.

Webb TH, Lilburne LR, Francis GS 2001. Validation ofthe GLEAMS simulation model for estimatingnet nitrogen mineralisation and nitrate leachingunder cropping in Canterbury, New Zealand.Australian Journal of Soil Research 39(5):1015-1025.

Webby RW, McCall DG, Blanchard VJ 1995. Anevaluation of the Stockpol™ model. Proceedingsof the New Zealand Society of Animal Production55: 145-159.

Wheeler DM 2009. OVERSEER scenarios—"what i fanalysis. In: Curries LD, Hanly JA ed. Nutrientmanagement in a rapidly changing world.Occasional Report No. 22. Palmerston North,New Zealand, Fertiliser and Lime ResearchCentre, Massey University.

Wheeler DM, Ledgard SF, de Klein CAM, MonaghanRM, Carey PL, McDowell RW, Johns KL 2003.OVERSEER® nutrient budgets—moving towardson-farm resource accounting. Proceedings ofthe New Zealand Grassland Association 65:191-194.

Wheeler DM, Ledgard SF, Monaghan RM, McDowell RW,de Klein CAM 2006. OVERSEER nutrient budgetmodel—what it is, what it does. In: Curries LD,Hanly JA ed. Implementing sustainable nutrientmanagement strategies in agriculture. OccasionalReport No. 19. Fertiliser and Lime ResearchCentre, Massey University. Palmerston North,New Zealand. Pp. 231-236.

Whitbread AM, Clem RL 2006. Graze to grain—measuringand modelling the effects of grazed pasture leys onsoil nitrogen and sorghum yield on a Vertosol soilin the Australian subtropics. Australian Journal ofAgricultural Research 57(5): 489-500.

White RE, Heng LK, Magesan GN 1998. Nitrate leachingfrom a drained, sheep-grazed pasture. II. Modellingnitrate leaching losses. Australian Journal of SoilResearch 36(6): 963-977.

White TA, Johnson IR, Snow VO 2008. Biophysicalsimulation modelling of pasture growth andcomposition under dryland and irrigated conditions.Grass and Forage Science: 63(3): 339-349.

Whiteman S, Brown HE 2009. Nitrogen managementfor environmental accountability—tools to assistsmart decision making. In: Curries LD, Hanly JAed. Nutrient management in a rapidly changingworld. Occasional Report No. 22. PalmerstonNorth, New Zealand, Fertiliser and Lime ResearchCentre, Massey University.

Wilson DR, Zyskowski R, Maley S, Pearson AJ 2004.A potential yield model for forage brassicasIn: Fischer T, Turner N, Angus J, McIntyreL, Robertson M, Borrell A, Lloyd D ed. 4thInternational Crop Science Congress, September2004, Brisbane, Australia.

Wilson DR, Reid JB, Zyskowski RF, Maley S, PearsonAJ, Armstrong SD 2006. Forecasting fertiliserrequirements of forage brassica crops. Proceedingsof the New Zealand Grassland Association 68:205-210.

Woods R, Elliott S, Shankar U, Schmidt J, BidwellVJ, Bright J, Harris S, Wheeler D, Ledgard S,Clothier BE and others 2004. The CLUES project:predicting the effects of land-use on water quality—stage I. NIWA Client Report for the Ministryof Agriculture and Forestry, project MAF04501.107 p.

Woods R, Elliott S, Shankar U, Bidwell VJ, Bright J, HarrisS, Wheeler D, Clothier BE, Green SR, Hewitt AEand others 2006. The CLUES project: predictingthe effects of land-use on water quality—stage II.NIWA Client report for the Ministry of Agricultureand Forestry, project MAF05502. 109 p.

Young P, Parkinson S, Lees M 1996. Simplicity out ofcomplexity in environmental modelling: Occam'srazor revisited. Journal of Applied Statistics23(2-3): 165-210.

260 New Zealand Journal of Agricultural Research, 2009, Vol. 52

Zhang B, Tillman R 2007. A decision tree approach tomodelling nitrogen fertiliser use efficiency inNew Zealand pastures. Plant and Soil 301(1-2):267-278.

Zyskowski RF, Wilson DR, Maley S 2004. A cohortmodel for simulating forage brassica crops withvariable plant size. In: Fischer T, Turner N, AngusJ, McIntyre L, Robertson M, Borrell A, LloydD ed. 4th International Crop Science Congress,September 2004, Brisbane, Australia.

Zyskowski RF, Jamieson PD, Li FY 2007. LUCI—from single season crops to continuoussequences. In: Kulasiri D, Oxley L ed. MODSIM2007—International Congress on Modellingand Simulation, December 2007, University ofCanterbury, Christchurch, New Zealand. Pp.1630-1637.