RIM: Anatomy of a Weed Management Decision Support System for … · 2015-11-10 · RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application

RIM: Anatomy of a Weed Management Decision Support System for Adaptationand Wider Application

Myrtille Lacoste and Stephen Powles*

RIM, or ‘‘Ryegrass Integrated Management,’’ is a model-based software allowing users toconveniently test and compare the long-term performance and profitability of numerous ryegrasscontrol options used in Australian cropping systems. As a user-friendly decision support system thatcan be used by farmers, advisers, and industry professionals, RIM can aid the delivery of keyrecommendations among the agricultural community for broadacre cropping systems threatened byherbicide resistance. This paper provides advanced users and future developers with the keys tomodify the latest version of RIM in order to facilitate future updates, modifications, and adaptationsto other situations. The various components of RIM are mapped and explained, and the keyprinciples underlying the construction of the model are explained. The implementation of RIM intoa Microsoft ExcelH software format is also documented, with details on how user inputs are codedand parameterized. An overview of the biological, agronomic, and economic components of themodel is provided, with emphasis on the ryegrass biological characteristics most critical for itseffective management. The extreme variability of these parameters and the subsequent limits of RIMare discussed. The necessary compromises were achieved by emphasizing the primary end-use of theprogram as a decision support system for farmers and advisors.Nomenclature: Annual rigid ryegrass, Lolium rigidum Gaud.Key words: Adoption, agriculture, bio-economic models, DSS, extension, herbicide resistance,management simulation, weed economics.

RIM, or ‘‘Ryegrass Integrated Management,’’ isa model-based decision support system (DSS) fortesting the biological and economic performance oftactics to control ryegrass (annual rigid ryegrass,Lolium rigidum Gaud.) in dryland broadacrecropping systems of the Australian southerngrainbelt. RIM offers users the option of custom-izing field characteristics such as grain yields, weedmanagement costs, and ryegrass control efficaciesbefore building a 10-yr strategy, thereby allowingthe testing and comparison of numerous controloptions on ryegrass numbers and crop fieldeconomic returns.

RIM’s first phase of development began in the1990s and culminated with the release of RIM’sfirst user-friendly version (Pannell et al. 2004b).RIM was used both as a research tool and as a DSS,primarily during workshops that successfully sup-ported the delivery of key herbicide resistancemessages to the agricultural community (Doole2008; Lacoste and Powles 2014). RIM uses ryegrasspopulation dynamics and allows users to explorescenarios in the long-term and at the field scale.

Unlike a majority of other weed managementmodels and DSS, RIM does not include automatedoptimization features (Holst et al. 2007) norprovide recommendations (e.g., Gonzalez-Andujaret al. 2011). Instead, users can easily experimentwith options and visually compare the consequencesof their management choices.

With the rise of herbicide-resistant weeds inglobal cropping systems, the need to communicateintegrated, diverse weed control practices to ensurecropping sustainability is increasingly important(Norsworthy et al. 2012). To continue contributingto this effort, RIM was upgraded and madeavailable online along with supporting documents(Australian Herbicide Resistance Initiative [AHRI]2013). The primary objective was to use theprogram as a DSS in order to continue theextension efforts pertaining to herbicide resistancemanagement. This redevelopment focused onupdating the program contents to more closelyreflect current farming practices and new technol-ogies, and on increasing the ease of use of the overallsoftware (Lacoste and Powles 2014). RIM defaultvalues, set to represent an average situation in theAustralian southern grainbelt, had to be easilymodifiable to better adjust to local situations. Theunderlying model was also restructured in order tofacilitate maintenance needs as well as future

DOI: 10.1614/WS-D-14-00163.1* Decision Support Agronomist and Winthrop Professor,

Australian Herbicide Resistance Initiative (AHRI), School of PlantBiology, University of Western Australia, Perth WA 6009, Australia.Corresponding author’s E-mail: [email protected]

Weed Science 2015 63:676–689

676 N Weed Science 63, July–September 2015

modifications. In particular, greater flexibilitywithin the model would also allow RIM to beadapted to other situations.

The original RIM model was presented inPannell et al. (2004b) and extensively described inPannell et al. (2004a) and Pluske et al. (2004),while numerous publications further examined itscore modelling notably through sensitivity analysis(see Lacoste and Powles 2014 and referenceswithin). A decade after the original release, thispaper provides one concise and updated descriptionof the entire program, to give advanced users andfuture developers the keys to modify the latestversion of RIM. Emphasis on the practicalities ofcoding and the reasoning behind option choiceswere preferred over equations and exact values, asthese were provided in the above-mentionedpublications for RIM and will vary for othercircumstances the model may be adapted to.

An overview of the program structure is pre-sented, before explaining how RIM is implementedin a software format using Microsoft ExcelH. Thenew locations of RIM components and parametersare mapped, and the conversion of user choices intomodelling elements is explained. The functions ofthe various components are then summarized,focusing on the biological, agronomic, or economiclogics that determine which elements, impacts, andlinkages are modelled. The strengths and limitationsof RIM’s modelling are then discussed, beforeconsidering promising avenues for possible adapta-tions and future developments.

Model Overview

Important Assumptions. RIM assesses the impactof weed control options on ryegrass populations andon gross margins, but does not provide exactpredictions. The results are trends to be evaluatedin a relative manner, keeping in mind the degree ofvariability inherent in many of the parameters onwhich the model is based upon. Additionally,several assumptions behind RIM include a numberof simplifications and exclusions:

N RIM is a deterministic model. Calculationsmodify yearly or field averages, without cateringfor annual or seasonal variations, environmentalfluctuations, or spatial heterogeneity (climate,rainfall patterns, control efficacies, soil, germina-tion, biomass growth, animal behavior, etc.).Default values are chosen to represent an averagesituation for the target area.

N Results are based on ‘‘long-term average weed-freegrain yields,’’ specified by the user. Each year, thefinal grain yields are obtained from the averageyield that is to be expected for the modelled fieldin an average season, when all other weeds arecontrolled. Biophysical mechanisms are not mod-elled; agronomic consequences are limited topredetermined rotational effects such as yieldbenefits or fertilizer savings.

N Crops and pastures are generic. Enterprises do notrepresent a given cultivar or species but rathera type of enterprise, defined by adjustablecharacteristics and management specifics.

N Net present values are the basis of long-termeconomic returns, which integrate some inflationmeasures.

N Weed genetics and the mechanisms of herbicideresistance evolution are not modelled. Instead, theuser decides whether a herbicide is effective or not.A situation with partial ryegrass resistance can thusbe represented by altering the control efficacy ofherbicides.

Inputs and Outputs. The inputs required by RIMmainly consist of (1) characterizing the modelledfield and context (e.g., average long-term yields,starting ryegrass density, establishment costs), (2)specifying the expected impact of various farmingpractices (e.g. ryegrass control, yield benefits, orpenalties), and (3) building a 10-year croppingsequence and defining the management options thatimpact ryegrass for each year. This is done bybuilding a strategy, choosing among seven enter-prise types and 44 management options, includingryegrass management practices at preseeding, in-crop, and postcrop maturity. With these informa-tion and choices, RIM provides the followingoutputs:

N Ryegrass plant and seed numbers (m22), calcu-lated at seven time points during the year.

N Yield loss (%) by ryegrass competition added toother yield benefits and penalties (%) from variouspractices, calculated at the end of each croppingyear.

N Economic returns ($ ha21), calculated both at theend of each year (gross margins) and for the 10-yrperiod (averaged net present value).

For screenshots of the program, illustrations ofresults, and how to use RIM, the reader is referredto the extensive examples provided in Lacoste andPowles (2014).

Lacoste and Powles: Anatomy of the RIM model N 677

Main Components. RIM was implemented inMicrosoft ExcelH for Microsoft WindowsH. Theprogram comprises several components, presentedin Figure 1 along with their major interactions.Figure 2 lays out the contents of the Excelspreadsheets that constitute these components.

N The user interface was designed as a straightfor-ward three-step progression: (1) Define Field, (2)Build Strategy, and (3) Compare Results. Addi-tional optional panels allow users to furthercustomize settings or export results.

N A VBA framework (Visual Basic for Applications)delivers a software-like behavior to the ensembleand provides the interface with various function-alities such as navigation, saving inputs, exportingoutputs, etc.

N A population dynamic model encompasses severalaspects of the ryegrass life cycle including

germination, plant and seed survival, intra- andinterspecific competition, seed production, andseedbank persistence.

N A rule-based model links all the components,attributing the proper set of parameters dependingon which management practices are specified bythe user.

Model Implementation

Two main layers can be distinguished that makeup RIM, the interface and the background, whichare complemented by macros.

Interface: Role and Structure, User Inputs,and Parameters. The two main tasks of theinterface are to integrate the user’s informationand choices, and to display the corresponding

Figure 1. Structure and relationships of the main RIM components. (Color for this figure is available in the online version of this paper.)


simulated results. The interface also performsspecific functions such as saving and loading inputs,as well as comparing and exporting outputs,provides instructions, help, contacts for technicalsupport, and further information. The interface ismade up of a series of spreadsheets referred to as

‘‘pages’’ or ‘‘panels,’’ partially protected fromchanges and formatted according to a consistentdesign that constitutes the visual identity of thesoftware.

The user starts by customizing a profile (field andcontext), which involves either modifying the

Figure 2. RIM implementation map: composition and linkages of Excel spreadsheets. The ryegrass population model is located in the‘‘Bio results’’ spreadsheet, whereas most of the rule-based model is in ‘‘Calc.’’ The Visual Basic Application (VBA) macros are coded in aninterface parallel to the spreadsheets (the VBE, Visual Basic Editor). (Color for this figure is available in the online version of this paper.)


preloaded default settings or starting a new profile.Only essential information is needed, including thelong-term average yields, herbicide efficacy, andprices for grain, fodder, sheep, herbicides, and anyuser-defined options. One of the optional panels(‘‘More Options’’) allows users to input furtherdetail for some practices, including setting theefficacy of additional ryegrass control methods,while the other panel (‘‘More Prices’’) allows usersto enter establishment costs and machinery repay-ments. Even though 600 parameters are integratedinto RIM, only around 50 are featured in theinterface (about 100 when considering the twooptional pages).

To ensure fast customization within the limitedscreen space, reduced user input is achieved throughseveral means:

N Hidden parameters: parameters deemed stable orrequiring expert knowledge to modify are onlylocated in the background, hidden from theinterface. In particular, this concerns biologicalparameters such as competiveness indicators,germination patterns, and other ryegrass variables.

N Combinations: whenever possible, inputs arecombined under a single label (e.g. ‘‘additionalinputs’’), or intermediary calculations skipped(e.g. fertilizer and herbicide costs are asked directlyinstead of detailing rates and unit prices, etc.). AVBA macro calls a calculator to help the userwhen such calculations are required.

N Ratios: one parameter set in the interface is oftenlinked to several others in the background by ratiotables. For instance, machinery costs were madeproportional to the harvester’s, while fertilizersavings the first year after a legume are halved thenext year. Similarly, the user sets pasture pro-duction levels for clover only, these values beingthen used for all other pastures types according tospecific proportionalities defined in the back-ground. This approach was also used to evaluatethe cost of nutrient removal for harvest options,the user being asked to evaluate a basic cost interms of input compensation following hay cut orresidue baling, which is linked to a reference tablewith nominal values.

N Links: whenever possible, links were madebetween different parameters. For instance, theincorporation cost of green manuring was as-sumed to be similar enough to the cost ofseedbank cultivation (full-cut establishment),while the costs of spring sprays where maderelative to the average cost of nonselective

herbicides specified by the user. The sameprinciple was used to link certain barley param-eters to wheat, corrected with ratios whennecessary (e.g., fertilizer requirements, yieldboosts).

After customizing settings, the user accesses thecore panel of RIM (step 2) where the ‘‘strategy table’’allows a 10-yr rotation to be built and outputs to beproduced. When incompatibilities exist betweenenterprises and management options, Excel’s auto-matic formatting prevents inputs from the users, orwarning error messages appear. Selecting an option inthe strategy table triggers a chain of commands in thebackground that ultimately lead to changes in theoutputs. The main outputs, ryegrass numbers, andgross margins at the end of each year are displayed atthe bottom of the strategy table and as graphs,allowing visual monitoring of the effects of modifyingthe 10-yr rotation. Additional graphs break downinformation summarized in the gross margins (type ofincome and of weed management expenses). Two setsof results can be ‘‘pasted’’ onto the following panelsfor comparison. This allows comparisons on the samescreen of, for instance, two different strategies withthe same initial profile settings but with differentmanagement choices (or vice versa), or two com-pletely different simulations. Comparisons produceadditional outputs pertaining to ryegrass populationand seedbank dynamics over the years, rotationaleffects and ryegrass burden on yields, and data tableson which all graphs are based. The last panel offers theuser the option of exporting detailed profile settingsor selected results to various file formats (to PDF,XPS, print preview, or Excel data tables). The user canthen choose to start again or exit the program.

Background: The DSS Backbone, Storage Unit,and Engine Room. Unlocking RIM from theCredits/Info page gives access to the backgroundlayer. Its first function is to host most of the DSS’components, including the calculations that makeup the submodels and their results, the parametersused by the models (user inputs, saved defaults, andreferences), and the material used by VBA macros:tutorial contents, saved information stored for laterload up (strategies and profiles), and indicatorsthat triggers various functions. These components,most of which appear as numerical tables, areconnected through Excel links, formulas, and macros.A color-coded key distinguishes apart customizableparameters, fixed references, critical values, andintermediary calculations.


The second function of the background layer is toperform the simulations. To do this, the mathemat-ics behind the modelling are translated into a formatusable by Excel through several steps. First, strategychoices are associated to codes in the ‘‘Calc’’ sheet(Figure 2). For instance, ‘‘0’’ is attributed to wheat,‘‘2’’ is canola, ‘‘53’’ stands for a barley crop that isthe second cereal after a 3-yr subclover pasturephase. No code means that a treatment was notselected and terminates the chain of command. Thecodes allow the model to choose the relevantparameters, directly from the interface or from thebackground when precalculations are required. Thisis done via two main types of Excel formulas,namely LOOKUP that refers to tables, and IF thatfollows logical steps. The latter is usually in the form‘‘IF-THEN-OTHERWISE.’’ Several such se-quences are frequently nested in order to accom-modate complex equations. The third step, which isoften partly embedded with the previous one,consists of calculations. The majority are performedin ‘‘Calc,’’ which contains most of the rule-basedmodel, before being finalized and summarized in‘‘Bio results’’ where most of the ryegrass population

dynamics is simulated, and in ‘‘Eco results’’ for theeconomic calculations. Regarding the latter, repay-ments and long-term averages use the PMT functionof Excel. Notes and in-built comments are providedto clarify the calculation linkages. The Excelfunctions ‘‘Trace Precedents’’ and ‘‘Trace Depen-dents’’ are also most useful.

VBA Macros: DSS Behavior. Some of the programfeatures and software-like behavior come fromexploiting the in-built functionalities available inthe latest Excel versions, such as cell comments,drop-down lists, and the advanced formattingoptions of cells and shapes such as ‘‘conditionalformatting’’ and ‘‘data validation.’’ However, mostof RIM’s responses were programmed through VBAmacros. The corresponding codes are located in theVBE, or Visual Basic Editor, a specific interfaceparallel to that of the spreadsheets. Table 1 lists thefunctions fulfilled by macros. Most of them aretriggered by clicking on interface buttons, others areautomatically prompted when opening or closingRIM. The macros were written with code compat-ible for the 2010, 2007, and 2003 versions of Excel

Table 1. RIM macros coded with Visual Basic Application (VBA).

Function Activation Description

Auto-entry andauto-exit

Automatic Locks and adapts the interface to any type of screen (full screen and auto-zoom, hides toolbars, various protections, no scrolling, splash sponsorsscreen, saving messages, restored options on closing).

Requirementschecking

Automatic Prompts warnings if the operating system is not Microsoft Windows and ifthe Excel version is older than 2007.

Lock/unlock Click on button in theCredits panel

Toggles program protection to access the background and parameters behindthe interface.

Tutorial Check tutorial box in theTitle panel before starting

Prompts help screens when visiting a panel for first time. Loads the defaultstrategy, clears the comparison function, and hides result graphs in thestrategy page.

Navigation Click buttons and RIMlogo on top of panels

Changes panels forth and back or returns to title screen, as well as resettingthe interface settings automatically when in lock mode.

Calculator Click on logo Calls the calculator to calculate herbicides rates, prices, etc.

Save/load/clear Click on buttons Keeps in memory up to five Profiles (including Prices and Options) and sevenStrategies.

Show graphs Click button in Strategypanel

Unhides or hides the result graphs and comparison buttons on the Strategypanel.

Comparison Click on one of the twobuttons in the Strategy panel

Freezes the current sets of results (on bottom or top-half of the screen) foreach of the four types of outputs. Two sets of results can be compared.

Graph scales Click on button Toggles vertical axis between ‘‘auto’’ (shows all values) and ‘‘fixed’’ (hideshigh values but allows easier comparisons).

Exporting Check boxes to select pagesand click on buttons

Converts selected pages to PDF and/or data tables to an Excel file. Errorhandling includes fall back on XPS format and print preview options. Fileauto-naming with date and time. Page set-up necessary once only for speed-up.


for MS WindowsH, using information availablefrom Walkenback (2010) and from various opensources available online.

Model Mechanisms

Ryegrass Population Model. Life Cycle. The modelformulation is based on the common iterativeprinciple for annual weeds where the currentpopulation is equal to the product of the previouspopulation size multiplied by rates reflecting in-trinsic and extrinsic factors (Holst et al. 2007). Theseedbank and plant density are updated separatelyseveral times a year rather than annually, essentiallyfollowing:

N seed density at the end of each period:Nt + 1 5 g(Nt)

N plant density at the end of each period:N’t + 1 5 f’(N’t) + g’(Nt)

N model initialization: N1 5 50 3 N’0

where g, g’, and f’ differ for each of the sevenperiods that define a year, and comprise rates relatedto emergence, natural mortality and control (whicheventually define seed dormancy or plant survival),and seed production. A time resolution shorter thanone year was used to account for the interactionsbetween the timing of practices and the ryegrassprotracted emergence pattern (Figure 3). Theemergence pattern, that is confounded with germi-nation for simplicity, was modelled with differentemerging rates for each time period, representingcohorts of different sizes. The model starts with N1,the initial ryegrass seed density in the soil seedbank.Given the practical difficulties of estimating thelatter compared to field scouting, the user is askedinstead to enter the density of ryegrass plants that setseed the previous spring (N’0). Not knowing thefield history, each survivor is assumed to result in anaverage of 50 viable seeds the following yearimmediately prior to the break of season (20 seedsnewly produced, 30 from residual seed bank).

Figure 3. Main mechanisms modelled in RIM. (Color for this figure is available in the online version of this paper.)


Seed Production. Although the model does notdistinguish biological stages per se (no biomass ortillering effects for instance), the phenologicalimportance of ryegrass fecundity was recognized ascritical to the infestation dynamic. Thus, seedproduction depends on the relative densities andcompetiveness of the crop and of ryegrass survivors,the time of emergence of the survivors (latergerminating cohorts produce fewer seeds per plantcompared to early-season escapes due to cropcompetition), and the sublethal effect of herbicides.Density-dependence is achieved through seed pro-duction, with a predetermined threshold formaximum seed production per ryegrass plant.

Yield Loss. The ryegrass burden on crops due tointerspecific competition is calculated througha proportion of weed-free yield, which integratesrelative plant densities and competitiveness of boththe crop and ryegrass. The resulting grain yield lossfollows a hyperbolic function plateaued by crop-specific maxima of the Cousen type (Pannell et al.2004b).

Ryegrass seed production and crop yield lossconstitute the core equations of the ryegrasspopulation model and have been explored, amongothers, by Doole (2008). Adjustments to theoriginal ryegrass model described in Pannell et al.(2004b) and Pluske et al. (2004) consisted of:initialization by seed density replaced by plantdensity as described above; plant and seed kill ratesadded to the iterative equations to account formouldboard ploughing; proportion of weed-freeyield used to calculate crop yield losses plateaued at100% to avoid higher numbers in the case of verylow ryegrass densities; translation of ryegrass bio-mass into hay yield deleted; sublethal effect ofnonselective herbicides on ryegrass fecundity addedfor late-season application of nonselective herbi-cides; values for maximum crop yield losses andryegrass germination pattern adjusted.

Crops and Pastures. The 10-yr land-use rotation(or sequence) can be built using seven enterpriseoptions: two cereal crops, an oilseed crop, a legumecrop, two improved pastures, and a volunteer pasture(resulting from volunteers from the existing seed-bank), which can also be managed as a chemicalfallow. Default crops and pastures were chosen torepresent the dominant enterprises of the targetregion, as well as to offer contrasted responses interms of production levels and ryegrass impacts. Forinstance, the two cereals differ in terms of yield

average, grain prices, levels of competiveness withryegrass, and maximum yield loss, whereas the twoimproved pasture types offer a choice betweena robust, reliable pasture versus a more productiveyet more fragile option. Unlike crops, the pastureoutputs are not ryegrass-dependent: responses tograzing and cutting are specified by the user, but arenot impacted by ryegrass competition. Duringa pasture phase (i.e. when grown continuously),stocking rates and ryegrass control levels increasebefore reaching a maximum after 3 years to reflectthe establishment phase. Besides production levels,ryegrass competitiveness or control, the enterprisesare defined by their establishment costs and by theirimpact on following crop(s) in terms of yield benefits(break effect), yield penalties (diseases), or nutrientrequirements. Regarding the latter, input savingsfollow nitrogen fixation from legumes, which forsimplicity are assumed to be similar for a legumecrop and a well-established legume-based pasture(e.g., grown for at least 3 years consecutively).

Also for simplicity, incompatibilities betweenenterprises and management options were kept toa minimum, but some were deemed unavoidable.For instance, seeding is not always required forpastures (e.g. volunteer and self-seeded pastures),crops cannot be grazed, and some harvest optionsare only available for crops. Other restrictionsprevent two oilseed or legume crops being grownconsequently because of high disease risks, and oneenterprise cannot be run for more than five times inthe 10-yr sequence. The latter acts as a reminder forthe important assumption that the average yieldsspecified by the user have to reflect a sustainable,long-term situation, i.e., featuring some diversity inthe farming system.

Management Options. Field operations and con-trol options affect ryegrass infestation levels byvarying crop density and competitiveness (rotationsand seeding rate), modifying the germinationpattern (soil preparation and crop establishment),reducing the number of survivors reaching maturity(modelled as a proportional kill of emerged plants)and their fecundity, and depleting the seedbank(soil preparation and harvest options). Options alsoincur changes in production costs (fertilizer savings,machinery repayments, environmental costs), aswell as yield advantages or penalties that are additiveto the ryegrass burden and rotational effects.

Seeding Times and Germination Patterns. The modelis constructed for the Mediterranean type of climate


of Australian southern grainbelt in which there isa hot, dry summer and an autumn–winter rainfedgrowing season. The break of season is defined asthe first autumn rains sufficient to wet the soil andallow acceptable crop germination after the summerdrought. Deciding when to seed the crop in relationto the autumn rainfall break determines whena number of field operations will occur in relation tothe sequential emergence of ryegrass (first fourperiods of the model; Figure 3) with several criticalconsequences:

N The later a treatment is applied, the more ryegrasswill have germinated, resulting in greater numbersof ryegrass plants controlled and greater depletionof the seedbank;

N However, early germinated ryegrass that maysurvive produce more seeds (competitive advan-tage), justifying action to control the first fewcohorts.

N Besides a number of agronomic consequences(such as yield penalty due to a shortened growingseason if seeding is delayed), the timing of seedingalso affects the cost and availability of somecombinations of options. For instance, dry seedingincurs a cost to account for increased erosion riskand, for the same reason, prohibits any prior soilpreparation. Waiting for sufficient rain to allowgermination and emergence of ryegrass can delaycrop seeding but allows the effective application ofnonselective herbicides, providing effective controlof the first emergence flush of ryegrass.

Establishment Options. The default establishmentsystem is based on a one-pass seeding operationincurring minimal soil disturbance (no-tillage),a practice that has dominated the region representedin the model for the past 20 years (Llewellyn et al.2012). Two options are modelled, which essentiallydiffer in cost, amount of ryegrass controlled, andimpact on the ryegrass germination pattern. Thegermination pattern is also impacted by prior soilpreparation, which in rare cases may includea shallow cultivation (tickle) or soil inversion(mouldboard plough). Shallow cultivation onlystimulates the germination of ryegrass withoutcontrol, whereas moulboard ploughing is a radicaloption targeting both ryegrass plants and seedbank,almost resetting the ryegrass population size to zero.For simplicity, given the relatively short life span ofryegrass seeds (Goggin et al. 2012), neitherdifferential seed redistribution throughout the soilprofile nor cultivation depths were modelled.

However, the possibility of returning buried viableseeds to the surface was taken into account witha lower proportion of kill when the durationbetween two ploughs was not sufficient to allowfor natural mortality to fully occur (i.e., 3 yr). Thisalso reflects the fact that mouldboard ploughing inno-till systems is a costly, tactical tool for which themain justification lies in removing subsoil con-straints. This was modelled via a one-off permanentyield benefit for all crops.

Herbicide Options. Three types of herbicides areincluded in the model: three preseeding nonselec-tive, five pre-emergence selective applied at seeding,and five postemergence selective (in-crop sprays).Default settings are provided; however, all 13herbicide options are entirely customizable by theuser who can specify denomination (e.g., mode ofaction, active ingredient(s), brand, tank mix, rate),enterprise compatibility, cost per hectare, andefficacy (% ryegrass control). The phytotoxic effectof selective herbicides on crops was modelled withan average yield penalty for each application. Tosimulate multiple applications of postemergenceherbicides, the user can select the same herbicide upto three times—having previously specified the costand efficacy of the herbicide option.

Spring Options. This period covers operationsoccurring before ryegrass seed maturation (i.e.,from around grain filling through to early maturityof the crop). The control options include grazing,crop/pasture-topping, and swathing. All options aremodelled as a proportion of ryegrass kill, withsurvivors of nonselective herbicides late applicationsalso causing reduced fertility and seed viability.

Several crop sacrifice options are also available.Crop sacrifice is a tactical decision resulting inlosing the crop or reducing the pasture income forthe benefit of achieving a complete seasonalryegrass kill for no seed production. To ensuretotal control, those tactics are usually followed bya high rate spray of a nonselective herbicidetargeting any survivors. Crop sacrifice is particu-larly justified in situations where meagre yieldscannot justify harvest costs, or where ryegrassnumbers are very high. Income may be salvaged inthe case of hay or silage production, although nopremium prices can be expected for foddercontaminated with ryegrass and trade-offs are tobe made with grazing intensity, especially forfragile pastures. In the case of green manuring,depending whether the operation is opportunistic


or planned in advance, costs such as crop insuranceand input savings can be specified. Anotheradvantage is potential yield benefit for thefollowing crop, if the sacrificed crop is terminatedearly enough and biomass is retained on site to savemoisture and nutrients.

Harvest Options. Burning field residues reduces theinput of weed seeds into the seedbank (modelled asa proportional seed loss). However, results arehighly variable. Additionally, the practice presentshigh risks of losing control of the fire and removessignificant amounts of biomass that potentiallydecreases the quantity of nutrients available forfuture crops. The loss of soil residue cover alsoincreases erosion risks. Harvest weed seed controltargets seeds during harvest more efficiently throughtechniques involving specialized equipment (Walshet al. 2013). Although some level of nutrient lossstill occurs in most cases, considerable amounts ofresidues are retained on site. The impact scope ofresidue removal on future crops is accounted forthrough nominal and relative values only, related tothe corresponding added fertilizer amounts that theusers deem adequate for their system. Thesenominal values were set between the HarringtonSeed Destructor (no removal) and whole-fieldburning (maximum removal). Although differenttechniques target different fractions of residues(chaff, or chaff and straw) with different methods(high intensity burn, export, crush, running over,and herbicide spray), the amounts of ryegrass seedremoved are similar (Walsh et al. 2013), withmanagement and environmental conditions such aswind strength or biomass amounts playing a greaterrole in the variation observed.

Economics. Each year, gross margin per hectare iscalculated from the final grain yield, pasture, orfodder incomes, minus the different variable costsassociated with each operation. Long-term calcula-tions compute net present values for machineryrepayments and for the 10-yr ‘‘average’’ economicreturn. The economic calculations integrate bankinterest, inflation rate, tax, potential yield increases,and discounting (PMT function of Excel). Fixedcosts, including capital costs, are not included sinceRIM considers an average farm that is alreadyequipped with essential machinery such as seedingequipment, harvester, and sprayer. Importantexceptions are harvest weed seed control techniques,involving specialist machinery which repaymentcosts need to be accounted for in order to compare

them with other weed management options. In thecase of chaff-tramlining, where the weed seeds stayon site but germination is prevented by repeatedcompaction on tramlines and localized herbicideapplications, the implementation costs of theguidance system is not accounted for since it isassumed that the main reason for its adoption is notweed control (near zero opportunity cost), butrather timeliness and ease of operations, a decisionmade at the whole-farm scale. Only the extra cost ofusing the system for weed control such as herbicidespray is therefore accounted for.

The revised seven enterprises and 44 manage-ment options are further detailed in Lacoste (2014),with default parameter values and costs set for anaverage farm of 2000 ha in 2012. A concise andillustrated version for end-users is also available(Lacoste 2013).

Model Strengths

Reasserting the Modelling Objectives: RIMUpdated as a DSS. RIM was upgraded primarilyfor use as a DSS, i.e., as a tool aiming to aidcomplex managerial decisions. The software fulfillsthis objective by allowing users to assess the impactsof various management strategies in a relativemanner. However, RIM only deals with a partialaspect of the herbicide resistance problem, and doesnot replace expert judgment and direct observations.As such, it should be remembered that RIM is nota forecast model aiming to provide exact predic-tions. RIM is built with compromises, withaccessibility taking precedence over representative-ness, simplicity over accuracy, and modellingefficiency over complexity.

Focus on Critical Ryegrass Characteristics. Theobjective of RIM was not to simulate complexbiophysical and environmental mechanisms, but tomonitor the impact of production and managementpractices on ryegrass populations within farmingsystems dominated by annual grain crops. Conse-quently, the rule-based model remained simple andthe ryegrass model was not further complexified.The choice of not venturing into additionalbiological intricacies was justified by the existingwell detailed ryegrass model, already deemedadvanced at the release of the 2004 version due toits integration of elaborated competition dynamics(Holst et al. 2007).

More generally, the strengths of the populationdynamic model lie in its focus on biological


characteristics, acknowledged as most critical toryegrass management. First among them, thenaturally high fecundity of ryegrass, influenced byboth intrinsic and extrinsic factors (Goggin et al.2012; Gonzalez-Andujar and Fernandez-Quinta-nilla 2004; Norsworthy et al. 2012), that wasmodelled via a core fecundity equation completedwith the latest information. It should be noted thatalthough biomass has been used to quantify theeffect of crop competition on ryegrass (Walsh andPowles 2007), its alteration was not modelledconsidering that seasonal build up and mobilizationof reserves are less critical for annual weeds (Holst etal. 2007). Other aspects of the ryegrass lifecyclewere considered to have critical managementimplications. These included seed losses anddormancy of the residual seedbank (Goggin et al.2012; Gonzalez-Andujar and Fernandez-Quinta-nilla 2004). The seedling emergence pattern isdetailed as well, since it informs the timing ofcontrol operations with regards to ryegrass life stagesand the necessity of controlling both early and lateemerging cohorts. Late-emerging weeds cause lessinterference with the crop and experience lowerreproductive success than early emerging cohorts,yet seed production from late season escapees mustbe considered when developing effective resistancemanagement strategies (Bagavathiannan and Nors-worthy 2012; Norsworthy et al. 2012; Walsh andPowles 2007).

Comparatively, biotic factors and pollen move-ments are considered less important, justifying thedecision to omit pest and disease populationdynamics, as well as complex mechanisms of seeddispersal. However, there is increasing evidence thatryegrass-contaminated grain seed can play a signifi-cant role in propagating resistant ryegrass (Michaelet al. 2010). No immigration component wasmodelled; instead, ryegrass densities never reachabsolute zero. This can demonstrate that even anapparently empty soil seedbank can rapidly result ininfestation due to external contamination.

Lastly, assuming that all other weeds arecontrolled via a cost that is specifiable may seemtoo simplistic, or unrealistic. In many cases, inter-weed interactions and associated control costs arenot to be underestimated. However, the priordevelopment of a multispecies RIM model thatcombined ryegrass and wild radish showed that,despite results being significantly impacted whenconsidering several weeds, the dominant weedspecies drives the economics of the system (Mon-jardino et al. 2003, 2005). Since ryegrass is the main

herbicide resistance challenge of the modelledsystem, a sole focus on this dominant weed specieswas deemed reasonable for this situation.

Model Limitations

Highly Variable Parameters. A major difficultywhen modelling ryegrass population dynamics isthat the most important characteristics are also thosewhich vary the most (Doole 2008). For instance,Goggin et al. (2012) reported that a reduction inryegrass seed fertility due to competition of thematernal plants ranges from 50 to 90%, whileSteadman et al. (2006) demonstrated how both seedproduction and seed viability are dependent on rateand timing of late-season nonselective herbicides.Gonzalez-Andujar and Fernandez-Quintanilla(2004) studied sites where very large fecunditydifferences were found, with values ranging from 7to 1,250 seeds per plant. Similar to this is theunpredictability of ryegrass emergence timing andextent, because of wide variation in dormancyresulting from the interaction of genetics, environ-ment, and other factors such as management history(Goggin et al. 2012). Examples include ryegrassestablishment ranging from 25 to 55% in SouthAustralia (Chauhan et al. 2007) and germinationlevels ranging from 0 to 70% across the WesternAustralian grainbelt with 20 to 80% at individualfarm-level (Goggin et al. 2012).

Need for Further Validation. In spite of suchinherent variations, the simulated low and mediumranges of ryegrass plant densities, i.e., the finalbiological outputs most important for management,were validated across 3 sites over 5 years (Draperand Roy 2002). Results also matched the trends oflong-term field observations made across theWestern Australian grainbelt (Walsh et al. 2013).Nevertheless, beyond one local validation andanecdotal evidence, further corroboration is re-quired to test the robustness of RIM (Hochman andCarberry 2011). Controlled experiments in researchstations are a valuable start (e.g. Gonzalez-Andujaret al. 2011). However, in order to be realistic, suchvalidation should also be conducted under farmingconditions, ideally on the long-term and over a vastarea of the target region. In addition to ryegrassdensities, yields should also be recorded, since themodel’s economic outputs are largely driven by thelong-term average yields and modelled yield losses.Validation would allow a test of whether thesimplifications and trade-offs chosen were justified,


particularly regarding the rule-based agronomicmodel. Specifically, it should be verified whetherthe relative impacts of the various control tech-niques are well represented, in spite of the inherentvariation to be found in an area as large and diverseas the Australian southern grainbelt. Additionally,and despite the argument that only weather-drivenmodels can be validated (Holst et al. 2007), a largevalidation would also indicate to what extentignoring the environment is justified. Althoughassuming average seasons is common in weedmanagement modelling (e.g., Lawes and Renton2010), not taking into account any changes ingrowth could have important consequences forintra- and interspecific competiveness (Holst et al.2007). The question is particularly legitimate inmany regions of the Australian southern grainbeltwhere high intra- and interyear rainfall variationsexist. Without integrating stochastic variability oreven renouncing the deterministic approach ofRIM, results could indicate whether outputs wouldbenefit from adding mechanisms that could accountfor extreme years.

Wider Application

The most likely future development of RIMwould be the use of the current version as a templatefor other annual weed species. Two examplesinclude the adaptation of the current RIM versionto brome grass for Australia (R. Llewellyn, personalcommunication), and to palmer amaranth in thecotton, corn, and soybean production systems of themid-southern U.S. crops (Bagavathiannan et al.2014). The task has been made easier during modelreconstruction. Adaptation would be easiest forannual weeds sharing characteristics similar toryegrass, i.e., dominant, prolific weed with relativelyshort seed dormancy and longevity. In some cases,specific additions may be required such as dispersal,biomass, immigration, some consideration forbiotic factors, or additional life history character-istics and management factors. In agriculturalsystems where cropping occurs both in winter andsummer, a factor to cater for the impact of thealternate cropping season may be a useful addition(for instance the impact of autumn operations onsummer germination patterns). Previous sensitivityanalysis conducted on the current populationdynamic model indicates that the biological weedparameters to which the most attention should bededicated to, besides the initial weed seed bank, arecrop-weed competiveness, emergence, fertility, and

maximum seed production; then again, economicvariables such as grain prices may prove to holdmore importance to the final outputs than bi-ological parameters (Lacoste and Powles 2014 andreferences within). Developers should thus remainwary of complexifying RIM much further. Theincreased modelling effort required to representsecondary mechanisms may overstep RIM’s level ofdetail, for little effect on the message delivered. Tothis end, other advanced mathematical models areavailable for a variety of weed species (e.g., Canneret al. 2009).

Future Modelling Directions

Beyond redefining the model’s base assumptions,possible paths to expand the current RIM version,or its adaptations, thus mostly include addingoptions and functionalities. However, future devel-opers should avoid partial updates which result in‘‘spaghetti’’ programming, i.e., bits of modellingoften complicating coding, holding redundancies,hiding errors, and requiring increased future effortin detangling (Walkenback 2010).

Additional Options. Enterprises could be added,but may come at the cost of simplifying the existingrotational system. Adding options and field opera-tions within the existing system would usually bea simpler avenue. Marginal options and practices ofinterest include wiper technology, germinationstimulant (Goggin and Powles 2014), encouragedant predation, use of animal selective eating habits,residue grazing, summer crops, and cover cropping(Goggin et al. 2012; Norsworthy et al. 2012). Thelatter options would require knowledge of the effectof retaining crop residues on germination patternsbecause of altered exposure and moisture retention.The same questions apply to the long-term effects oftactical mouldboard ploughing in no-till systems.Regarding establishment, disc-seeding would repre-sent a valuable no-till option. The resulting minimalsoil disturbance would require adjusting both theryegrass germination and the efficacy of some pre-emergent herbicides that are conditioned by soilincorporation (Chauhan et al. 2006, 2007).

Additional Functionalities. Another avenue wouldbe to improve profile customization. For instance,a simple herbicide performance component couldbe added to simulate tolerance to herbicides orsuboptimal spraying conditions. Similarly, the usercould be given the option of manipulating the


competitiveness of crops so as to represent givencultivars with more competitive genotypes. The usercould also customize the name and other aspects ofthe generic crop and pastures in the interface,without having to go into the background calcula-tions.

Farmers have a high interest in investigating riskyor uncertain events (Hayman 2004; Hochman andCarberry 2011). Adding stochastic elements wouldrequire too much development effort and defeat thefact that RIM purposefully ignores the environmentand weather forecast aspects. Nevertheless, a simpletool could be added in RIM to allow users toevaluate the impact of nonaverage years. Forinstance a ‘‘drought’’ option in the strategy tablecould vary crop yields, ryegrass competitiveness,seedling mortality and fertility, while drasticallymodifying the effects of tactical decisions for thegiven year. Another path to explore could be todevelop the sensitivity analysis function of RIM: anadditional output page could be developed allowingto assess the effects of some parameter changes onlong-term profitability.

Another functionality of interest could be a di-versity indicator. Currently, the budget allocationsegregates weed control methods into chemical,mechanical, competition, and user-defined types.This provides an appraisal of the diversity ofpractices implemented, albeit economically biasedsince a number of mechanical methods are muchmore expensive than most nonselective herbicides,irrespective of control levels. Furthermore, culturalaspects such as rotational diversity are not taken intoaccount. The principle could be expanded to moreaccurately evaluate the extent to which the userfollows the recommendation of diversifying prac-tices so as to provide more sustainable weed controlstrategies.

In contrast, developing an actual indicator for therisk of evolving herbicide resistance would requiremuch further modelling effort and the reintroduc-tion of herbicide modes of actions, which werepurposefully removed (see Lacoste and Powles2014). Moreover, several such indicators alreadyexist, such as those described in Stanton et al.(2008) and in Werth et al. (2011) for glyphosate usein Australia.

Next Step: Evaluation. In spite of the interest thatsome of those development paths may raise, furtherinvestment in RIM as a DSS should be prioritizedto validation and evaluation. The first wouldcomplement previous sensitivity analysis and help

identify which aspects of the rule-based modelshould be expanded, while the second would helpassess which development efforts would be mostvalued by the end-users. So far RIM has benefitedfrom the inputs of economists, weed biologists, andagronomists. New functionalities could be identi-fied by communication experts.

The rise of herbicide resistance weeds is anincreasing challenge to the agricultural industry, inAustralia and worldwide. Consequently, communi-cation and extension efforts to advocate sustainablepractices are increasingly needed.

RIM is a unique tool of considerable potential tocontribute to this endeavor. For this reason, RIM’sunderlying modelling was upgraded with theobjective of minimizing future investments in furtherdevelopments. Modifying or adapting RIM wasmade more straightforward than for most simulators,and therefore accessible to most, with minimaltraining requirements. The main challenges reside inclarifying the assumptions the model relies upon,and deciding to which level of detail the biologicaland agronomic mechanisms have to be modelled.The decision rule on such matter is that anymodelling effort should be subordinated to theend-use of RIM as a decision support tool, not theopposite. This implies that modelers should firstverify whether modifications alter recommendations.Then, care should be dedicated to assess how thedelivered messages are perceived by the users.Therefore, future RIM developers should rememberthat beyond mathematical and coding efforts,modelling a DSS also implies integrating user-centered and postdevelopment considerations.

Acknowledgments

This work was funded by the Grains Research andDevelopment Corporation of Australia (GRDC). Theauthors thank all of the many individuals from variousinstitutions who contributed to RIM throughout itsdevelopment, particularly from the University ofWestern Australia (notably from the Australian Herbi-cide Resistance Initiative and the School of Agriculturaland Resource Economics) and from the Department ofAgriculture and Food of Western Australia. The authorsalso thank Muthukumar Bagavathiannan and GayleSomerville for comments on the manuscript, andChristopher Preston for his support.

Literature Cited

AHRI Australian Herbicide Resistance Initiative (2013) RIM:Ryegrass Integrated Management–Australian Herbicide


Resistance Initiative, The University of Western Australia.http://www.ahri.uwa.edu.au/RIM. Accessed August 15, 2014

Bagavathiannan MV, Norsworthy JK (2012) Late-season seedproduction in arable weed communities: management im-plications. Weed Sci 60:325–334

Bagavathiannan MV, Norsworthy JK, Lacoste M, Powles SB(2014) PAM: a decision support tool for guiding integratedmanagement of palmer amaranth in Proceedings of the WeedScience Society of America 2014 Conference, Vancouver.http://wssaabstracts.com/public/22/proceedings.html. AccessedAugust 15, 2014

Canner SR, Wiles LJ, Erskine RH, McMaster GS, Dunn GH,Ascough JC (2009) Modeling with limited data: the influenceof crop rotation and management on weed communities andcrop yield loss. Weed Sci 57:175–186

Chauhan BS, Gill G, Preston C (2006) Influence of tillagesystems on vertical distribution, seedling recruitment andpersistence of rigid ryegrass (Lolium rigidum) seed bank. WeedSci 54:669–676

Chauhan BS, Gill GS, Preston C (2007) Effect of seedingsystems and dinitroaniline herbicides on emergence andcontrol of rigid ryegrass (Lolium Rigidum) in wheat. WeedTechnol 21:53–58

Doole GJ (2008) Increased cropping activity and herbicideresistance: the case of rigid ryegrass in Western Australiandryland agriculture. Pp 1–40 in Berklian YU, ed. CropRotation: Economics, Impact, and Management. Hauppauge,NY: Nova Science Publishers

Draper AD, Roy B (2002) Ryegrass RIM model stands the test ofIWM field trial data. Pp 49–50 in Proceedings of AgriBusinessCrop Updates 2002. Perth, Australia: Department ofAgriculture and Food Western Australia

Goggin DE, Powles SB (2014). Fluridone: a combinationgermination stimulant and herbicide for problem fields? PestManag Sci 70:1418–1424 DOI: 10.1002/ps.3721

Goggin DE, Powles SB, Steadman KJ (2012) UnderstandingLolium rigidum seeds: the key to managing a problem weed?Agronomy 2:222–239

Gonzalez-Andujar JL, Fernandez-Quintanilla C (2004) Model-ling the population dynamics of annual ryegrass (Loliumrigidum) under various weed management systems. CropProtection 23:723–729

Gonzalez-Andujar JL, Fernandez-Quintanilla C, Bastida F,Calvo R, Izquierdo J, Lezaun JA (2011) Assessmentof a decision support system for chemical control of annualryegrass (Lolium rigidum) in winter cereals. Weed Res 513:304 –309

Hayman PT (2004). Decision support systems in Australiandryland farming: a promising past, a disappointing present anduncertain future. In Proceedings for the 4th InternationalCrop Science Congress. Brisbane, Australia

Hochman Z, Carberry PS (2011) Emerging consensus ondesirable characteristics of tools to support farmers’ manage-ment of climate risk in Australia. Agricult Sys 104:441–450

Holst N, Rasmussen IA, Bastiaans L (2007) Field weedpopulation dynamics: a review of model approaches andapplications. Weed Res 47:1–14

Lacoste M (2013) RIM, Ryegrass Integrated Management–Userguide. Australian Herbicide Resistance Initiative, The Univer-sity of Western Australia, Perth. 9 p. www.ahri.uwa.edu.au/RIM. Accessed August 15, 2014

Lacoste M (2014) RIM 2013: default settings. AustralianHerbicide Resistance Initiative and School of Agricultural

and Resource Economics, The University of WesternAustralia, Perth. http://www.ahri.uwa.edu.au/RIM. AccessedAugust 15, 2014

Lacoste M, Powles SB (2014) Upgrading the RIM Model forimproved support of integrated weed management extensionefforts in cropping systems. Weed Technol 28:703–720

Lawes R, Renton M (2010) The Land Use Sequence Optimiser(LUSO): a theoretical framework for analysing crop sequencesin response to nitrogen, disease and weed populations. CropPast Sci 61:835–843

Llewellyn RS, D’Emden FH, Kuehnea G (2012) Extensive use ofno-tillage in grain growing regions of Australia. Field Crop Res132:204–212

Michael PJ, Owen MJ, Powles SB (2010) Herbicide-resistantweed seeds contaminate grain sown in the Western Australiangrainbelt. Weed Sci 58:466–472

Monjardino M, Pannell DJ, Powles SB (2003) Multispeciesresistance and integrated management: a bioeconomic model forintegrated management of rigid ryegrass (Lolium rigidum) andwild radish (Raphanus raphanistrum). Weed Sci 51:798–809

Monjardino M, Pannell DJ, Powles SB (2005) The economicvalue of glyphosate-resistant canola in the management of twowidespread crop weeds in a Western Australian farmingsystem. Agric Sys 84:297–315

Norsworthy JK, Ward SM, Shaw DR, Llewellyn RS, NicholsRL, Webster TM, Bradley KW, Frisvold G, Powles SB, BurgosNR, Witt WW, Barrett M (2012) Reducing the risks ofherbicide resistance: best management practices and recom-mendations. Weed Sci 60:31–62

Pannell D, Stewart V, Bennett A, Monjardino M, Schmidt C,Draper A, Powles SB (2004a) RIM 2004–User’s manual.Crawley, Australia: The University of Western Australia. 49 p

Pannell DJ, Stewart V, Bennett A, Monjardino M, Schmidt C,Powles SB (2004b) RIM: a bioeconomic model for integratedweed management of Lolium rigidum in Western Australia.Agric Sys 79:305–325

Pluske JM, Pannell DJ, Bennett AL (2004) RIM 2004 ReferenceManual. A Decision Tool for Integrated Management ofHerbicide-Resistant Annual Ryegrass. Crawley, Australia:School of Agricultural and Resource Economics, Universityof Western Australia. 46 p

Stanton RA, Pratley JE, Hudson D, Dill GM (2008) A riskcalculator for glyphosate resistance in Lolium rigidum (Gaud).Pest Manag Sci 64:402–408

Steadman KJ, Easton DM, Plummer JA, Ferris DG, Powles SB(2006) Late-season non-selective herbicide application reducesLolium rigidum seed numbers, seed viability, and seedlingfitness. Aust J Exp Agric 57:133–141

Walkenback J (2010) ExcelH 2010 Power Programming withVBA. Hoboken, NJ: Wiley Publishing, Inc.1052 p

Walsh MJ, Powles SB (2007) Management strategies forherbicide-resistant weed populations in Australian drylandcrop production systems. Weed Technol 21:332–338

Walsh M, Newman P, Powles S (2013) Targeting weed seedsin-crop: a new weed control paradigm for global agriculture.Weed Technol 27:431–436

Werth J, Thornby D, Walker S (2011) Assessing weeds at risk ofevolving glyphosate resistance in Australian sub-tropical glypho-sate-resistant cotton systems. Crop Past Sci 62:1002–1009

Received October 20, 2014, and approved February 3,2015.


Documents

RIM: Anatomy of a Weed Management Decision Support System for … · 2015-11-10 · RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application