SEA Working Paper 00/10

RIM: A Bioeconomic Model for Integrated Weed Management

David J. Pannell, Vanessa Stewart, Anne Bennett, Marta Monjardino, Carmel Schmidt and Stephen Powles

Abstract

The RIM (Ryegrass Integrated Management) model is presented. RIM represents a wide diversity of herbicide and non-herbicide based weed management options, in the context of the non-irrigated extensive farming system of southern Australia. The enterprise choices include cereals, lupins, canola and three types of pastures for grazing by sheep. Users of RIM may specify the enterprise sequence and any feasible combination of the 35 weed treatment options each year over 10 or 20 years. Weed treatment options include selective herbicides (11), non-selective herbicides (5), non-chemical treatments (16) and user-defined treatments (3). The model represents weed and seed bank population dynamics, weed-crop competition, weed treatment impacts (including phytotoxicity), agronomic details, and financial details. Economic and biological model results are presented for scenarios with differing levels of availability of selective herbicides and different rotational sequences.

INTRODUCTION

Since 1975, farmers in Australia’s extensive dryland agricultural systems have come to rely heavily on herbicides for weed control (Sindel, 2000). However, during the 1990s, the phenomenon of herbicide resistance in prominent crop weeds has increased dramatically (Walsh et al., 2001; Llewellyn and Powles, 2001). Annual ryegrass (Lolium rigidum), which has long been the most economically important weed of crops in southern Australia (Pannell, 1990a, 1990b; Abadi Ghadim and Pannell, 1991) is the world’s most severe example of herbicide resistance. Across much of southen Australia annual ryegrass exhibits multiple herbicide resistance across different herbicide modes of action (as exemplified in Burnet et al., 1994, and reviewed in Preston and Powles, 2000). Multiple resistance occurs as a result of selection pressure from application of multiple herbicide groups, or through generic mechanisms bestowing "cross resistance" to a range of chemical types, including types which have not been applied to the weeds.

The consequence of multiple and cross resistance to many herbicides is that farmers are not able to maintain weed control by alternative herbicides and are required to introduce on alternative methods of weed control. The alternatives include a return to practices that had been minimised as a result of herbicide availability, as well as innovative new practices. They include systems that rely on ecological processes, physical weed control methods, and a reduced range of chemical herbicides of types that are less prone to resistance (Powles and Bowran, 2000). Thus, by necessity, many Australian farmers are adopting diverse combinations of weed control measures, consistent with the concept of "integrated weed management" (IWM). This makes them unusual amongst large scale, commercial farmers of the developed world, apart from those embracing so-called "organic" farming methods.

However, farmers face a number of difficulties in their decision making about IWM strategies:

• The farmers are unfamiliar and inexperienced with a number of the control options;
• Strategies must be evaluated over the longer term, not just for a single year;
• The long-term impacts of multiple control options are difficult to predict;
• The impacts of individual treatments within an integrated strategy are difficult to interpret from field observations;
• Some strategies have indirect, as well as direct costs; and
• There is a vast number of possible combinations of treatments to be considered.

Given these difficulties, IWM seems a topic for which a computerised decision support system could be especially valuable to both farmers and farm advisors. To date, decision support systems for weed management have focused primarily on herbicides and most have had a relatively short-term focus (e.g. Doyle, 1997). The RIM model, described below, appears to be the only example of a system that represents a comprehensive set of weed control treatments, including both herbicide and non-herbicide options.

Applications of early versions of RIM are presented by Stewart (1993) and Schmidt and Pannell (1996a, 1996b). Simpler models that pre-dated and led to the development of RIM are described by Gorddard et al. (1995, 1996). Apart from these papers, there has been little published on economic aspects of herbicide resistance, an exception being Orson (1999).

The next section includes an overview of the model and its development, followed by a description of the various components of RIM, including biological, economic and agronomic components. The model is used to evaluate the impacts of reducing herbicide availability for the selection of weed control practices and to assess their economic consequences. Results from these analyses are presented and discussed.

MODEL DESCRIPTION

Overview

Underlying RIM is a dynamic simulation model. The model is deterministic and integrates economic, biological and agronomic components. For economic aspects, the time step is annual. For biological processes, particularly weed population dynamics, seven periods of the year are defined (see below). The model is implemented in a spreadsheet program, Microsoft Excel ®, using formulae and Visual Basic macros.

The model includes approximately 500 parameters (biological, agronomic and economic) which are adjustable by users. Specification of values for each of these parameters was a major task in the development of RIM. Sources of data and information were numerous and diverse. Economic parameters were obtained from an existing whole-farm economic model (Morrison et al., 1986; Kingwell and Pannell, 1987; Pannell, 1996), and updated from budget guides published for farmers. Parameters for control effectiveness of weed control options were estimated based on long-term field experiments designed to evaluate their effects (Bill Roy, pers. comm.) and from other field trials conducted by the state government agriculture agency, Agriculture Western Australia. Parameters for weed competition functions were calibrated in cooperation with weed scientists in Agriculture Western Australia to provide relationships consistent with field trial evidence.

RIM is a decision support system – it is designed to provide information and insights to farmers to help them in their long-term decision making about management of ryegrass, the most important weed of crops in southern Australia. RIM allows the user to simulate many different combinations of weed control treatments and observe their predicted impacts on ryegrass populations, crop yields and economic outcomes.

RIM represents a single field. The user can specify whether or not the ryegrass population in the field is resistant to each herbicide group, or how many applications of herbicides from each group are available before resistance will develop. This implies a sudden loss of herbicide efficacy, which approximates the reality of herbicide resistance development by annual ryegrass in southern Australia. A wide variety of non-herbicide weed treatment options is included, so that as herbicides are lost, the best substitute treatments can be identified.

RIM is useful for a number of different types of users, including:

• farmers attempting to make decisions for their own weed management;
• private consultants, extension agents or agribusiness agronomists wishing to provide advice to their clients;
• facilitators running RIM with groups of farmers;
• scientists, students and others wishing to understand the management of herbicide-resistant ryegrass.

The enterprise options available for users to select are wheat, barley, canola, lupins, volunteer pasture, subterranean clover pasture, and cadiz pasture. The user may select these in any agriculturally feasible sequence. There are inter-year impacts of one enterprise on another, depending on the sequence selected. For example, a cereal crop grown after a legume crop or pasture benefits from a higher yield and a reduced requirement for nitrogen fertilizer (Pannell, 1995a, 1998).

Details of assumptions and parameters of the model are provided by Pannell and Bennett (2000). The following description provides an overview.

Biology

Weed population dynamics

RIM represents the key factors which drive the pattern of weed population change over time, as follows:

• Initial weed seed density in the soil.
• The timing of weed seed germination relative to the crop. Later germinating weeds produce fewer seeds per plant.
• Natural mortality of weeds and seeds.
• Seed production per plant.
• Impacts of weed and crop densities on seed production per plant.
• The timing and effectiveness of treatments to reduce weeds or seeds.

For this purpose, in the simulation model the year is broken into seven periods:

• First rains of the growing season which allow crop sowing,
• Seeding to 10 days later,
• 11 to 20 days after seeding,
• up to time of post-emergence herbicide application (if selected),
• post-emergence spraying to mid spring,
• mid spring to harvest, and
• harvest to opening rains of the next season.

Weed numbers (m^-2) and weed seed numbers in the soil (m^-2) are recorded at the end of each of these periods.

Competition between weeds and crops

The yield of a crop depends on the relative competitive abilities of that crop and of ryegrass, and the densities of each. The standard competition relationship for wheat yield as a function of ryegrass density is shown in Fig. 1. The function is illustrated for two wheat plant densities: 100 plants m^-2 (typical of current farming practice) and 160 plants m^-2.

Figure 1. Impact of weed competition on wheat yield.

The functional form underlying this relationship is

(1)

where Y is crop yield (as a proportion of the weed-free yield), P₀ is a standard crop density, P₁ is the actual crop density, W is the density of weeds surviving all treatments, M is the maximum proportion of grain yield lost at very high weed densities, a is a constant which depends on the crop, and k is a constant reflecting the competitiveness of the weed on the particular crop. For wheat in competition with annual ryegrass, the default values are as follows: P₀ = 100, M = 0.60 (based on results of Pannell, 1990a; Pannell, 1995b; Pannell and Gill, 1994), a = 5, and k = 0.33. This competition function is similar to the widely used hyperbola of Cousens (1985) but is more flexible in that it allows representation of different crop densities.

Crop-related variables

The following crop-related variables are represented.

• Standard weed-free yields for crops after a break of at least three years without a legume.
• Yield boosts for cereals after legumes, canola or a pasture.
• Yield effects on crops of green manuring and swathing.
• Yield effects from disease in short rotations.
• Seeding rates.
• Savings in nitrogen fertiliser following lupins and pasture.
• Impacts of delayed seeding on yield.
• Parameters of the competition functions.
• Phytotoxic effects of herbicides and some physical control measures on each crop.

Pasture-related variables

There are three types of pasture represented in RIM: a volunteer pasture, a phase pasture (assumed to be cadiz serradella) and a regenerating pasture (assumed to be subterranean clover). For some purposes, cadiz and sub-clover are treated as being equivalent (e.g. weed treatment effectiveness, weed treatment costs).

RIM does not include detailed simulation of the population dynamics for each possible pasture species, so the biological impacts of a pasture phase on ryegrass populations are represented in a relatively simple way. For each type of pasture, the impact on ryegrass seed density under standard and high intensity grazing conditions is specified by the user. The standard reduction in weed seeds is greater in a second or third consecutive year of pasture because the non-ryegrass components of the pasture stand are denser and more competitive at these stages.

Economics

Users of RIM quickly come to appreciate the importance of taking a long-term view on the economics of weed management. RIM highlights the potential for long-term benefits from short-term economic sacrifices. It allows the user to assess such trade-offs in a balanced way. The question of whether a preventative strategy is economic in the long term depends on a host of factors, including the cost of the strategy, its impact on weeds, prices of outputs and the initial weed seed density.

Taking a long-term view of economics poses some problems. In particular, how should one assess the overall economics of a strategy for which the gross margin changes from year to year? How can one validly compare costs and benefits that occur in different years, given the complexities of interest, tax, price trends and trends in yields? The standard approach used by economists and financial analysts to assess long-term investments involves a process called "discounting", which allows all costs and benefits to be expressed in the equivalent of their present day value (Robison and Barry, 1996). The costs and benefits of all strategies of interest would be discounted and summed to determine the Net Present Value (NPV), and the preferred strategy would be that with the highest NPV.

If the discount rate used is the bank interest rate (which is common practice), then this process is equivalent to identifying the strategy which would result in the highest bank balance at the end of the period (assuming that all income is deposited in the bank account and accumulates interest, and all costs are withdrawn from the bank account and reduce the amount of interest earned). This "final bank balance" approach is the method used in RIM because we believe that most people find it easier to understand. The approach also makes it easier to include some realistic complexities that are often ignored in long-term financial analyses. RIM includes each of the following complexities in its calculations.

(a) Tax is paid on interest earned. We represent the tax system simply, because there is so much variability between farmers in their tax arrangements. RIM allows the user to specify a single marginal tax rate, which should be the rate of tax he or she would pay on any additional income earned above current income.

(b) The inflation rate on sale prices in agriculture has historically been lower than the inflation rate on input purchase prices. This is commonly referred to in Australia as the "cost-price squeeze" and in North America is recognised as the cause of the "farm problem". It is the reason why farmers have had to improve their productivity levels in order to remain in business. We assume that this trend will continue for the time being so we set the inflation rate on crop and sheep product prices lower than the assumed inflation rate on input costs.

(c) Yields increase over time. This is hard to predict, but over the long term is a very significant factor. In the standard RIM the annual rate of yield increases is set to 1.0 percent for crops and 0.5 percent for sheep products. These relate to yield per hectare, not per sheep.

Treatment options

There are a total of 35 different weed treatment options included in RIM (Table 1). They can be broken into four separate groups: selective herbicides (11), non-selective herbicides (5), non-herbicide treatments (16) and user-defined treatments (3). Further details of a sub-set of the treatments are provided in the Results and Discussion section.

Table 1. Weed treatment options included in the RIM model.

* N = Non-selective herbicide, S = Selective herbicide, B = "Biological" treatment (non chemical)

In the case of herbicides, the user can specify the number of applications available for each herbicide group, prior to the onset of resistance. If ryegrass is fully resistant to a herbicide group, the limit for that group is set to zero.

Limitations

RIM will not automatically calculate which strategy is "best". Users evaluate strategies using experimentation and "trial and error".

RIM does not represent year-to-year variation in weather, potential yield or herbicide performance. Yields in the model do vary from year to year due to the sequence of crops and pastures selected, and the level of weed competition. Climatic conditions do not rule out any of the treatment options. Users can self-impose constraints on the use of different treatments.

RIM represents only a single field. Some strategies may involve changes in machinery or livestock management that have impacts at the whole-farm level. Similarly, RIM makes particular assumptions about the way that investments in machinery are financed. Farmers may need to further consider whole-farm cash flow implications of strategies outside of RIM before making adoption decisions.

Although considerable effort has been expended on data collection, there are still areas where the available information is relatively weak. This seems inevitable in such a comprehensive model. Sensitivity analysis (Pannell, 1997) is an important approach for evaluating the significance of data deficiencies. A related issue is the variation in biological and economic parameters between farms. The values included in the standard version of RIM are representative of a typical farm in a region of Western Australia, but need adjusting for other farm types and for other regions. Users can readily alter the parameter values to suit their particular situation.

RESULTS AND DISCUSSION

We present two sets of results to illustrate the use of RIM to evaluate weed management alternatives. The first results show the implications of different levels of availability of a selective herbicide, assuming that the sequence of crop or pasture enterprises is held constant. In the second set of results, herbicide availability is unaltered, but the rotational sequence is altered. In each case, a time frame of 10 years is used. RIM has a maximum time frame of 20 years.

Comparison of high and low herbicide strategies

Table 2 shows the results of reducing usage of a selective herbicide over a 10-year period. The reduced usage might be for either of two reasons: (a) that the herbicide has been used in the past, so that a smaller number of applications remains available before the onset of resistance, or (b) that the farmer wishes to conserve applications of the herbicide for future periods. The scenario is simplified for illustrative purposes. It is based on the assumption that ACCase inhibiting herbicides (so called "fops" and "dims") are the only selective herbicides available. No constraints are placed on the use of non-selective herbicides or non-chemical treatments, other than those that are required agriculturally. Results are shown for different intensities of use of the selective herbicide, ranging from 10 uses over the 10 years down to 2 uses. The lupin-wheat cropping rotation is used throughout.

Table 2. Consequence of restricting usage of selective herbicides over 10 years.

^AThe number of years in which this treatment was applied is shown in brackets.

For each level of herbicide availability, Table 2 lists the set of additional treatments which are most profitable. As noted earlier, RIM is not an optimisation model, so these treatments were identified by a process of extensive experimentation with the model. Included in the results are:

• High crop seeding rates. Increasing the density of wheat plants from 100 to 160 plants m^-2 and of lupin plants from 40 to 66 plants m^-2. The higher density of crop plants provides increased competition against weeds (Barrett and Cambell, 1973; Medd et al., 1985; Lemerle et al., 1995; Tanji et al., 1997; Cousens and Mokhtari, 1998), increasing crop yield and reducing weed seed production.
• Paraquat applied post-anthesis to lupins. To minimise weed seed production, the non-selective herbicide paraquat can be sprayed late-season in-crop in lupins or applied to pasture just before weed seeds mature (Powles and Matthews, 1996). If sprayed too early it can severely damage the crop or pasture but if timed correctly this practice can result in a substantial reduction in seed production, with tolerable levels of crop damage (20 percent reduction in lupin yield is assumed). The standard seed reductions assumed in RIM are 80 percent in lupins and 85 percent in pasture, based on an observed range of 50 to 90 percent (Gill, Holmes and Kelly, 1994).
• Seed catching at harvest. Harvesting is an important opportunity to reduce the number of ryegrass seeds entering the ryegrass seed bank. Ryegrass seed remains attached to the plant at the time of grain harvest and is therefore harvested along with the crop. It is estimated that 75 percent or more of the ryegrass present at harvest passes through the harvester and exits the machine to the soil. However, about 5 percent of farmers are implementing measures to capture this exiting weed seed ("seed catching"). Seed catching collects all the seed and chaff from the harvester’s top sieve into a cart towed behind the harvester. When the cart is full, the contents are dumped in piles, which can then be burnt or removed. In RIM the weed control rate of seed catching has been set at 60 percent if only dumps are burnt or 68 percent if a total burn is conducted. The observed kill range in the field is 40 to 80 percent.
• Cultivation and delayed seeding. A shallow cultivation is often used in Western Australia and is the cultivation method assumed in RIM. This practice buries weed seeds to a depth of one to two cm enhancing weed germination by increasing contact with soil moisture. A delay between this cultivation and seeding is necessary to give an opportunity for the weeds to germinate and then be killed using a non-selective herbicide (glyphosate in this case). The longer the delay in seeding, the more weeds germinate and the higher the kill. However, a yield penalty is experienced when seeding is delayed. In RIM, seeding can be delayed by 10 or 20 days. Different yield penalties are specified for different crops.

Table 2 indicates that, as herbicide availability increases, the optimal total number of weed treatments other than selective herbicides falls substantially and steadily. As herbicide usage increases from 2 to 10 applications over the 10-year period, the optimal number of additional treatments falls from 35 to 13. This reflects the relatively high effectiveness of selective herbicides. A combination of numerous non-selective treatments is used to replace them if they are not available.

Interestingly, RIM reveals that well-designed, economical strategies involving less reliance on selective herbicides result in almost the same average density of weeds as do herbicide-dominant strategies. Despite the lower efficacy of the alternative treatments, it is economical in the long run to combine treatments such that high control of weeds is achieved. This is consistent with survey results in Western Australia, which have found that weed densities in farmers’ fields with herbicide resistance are, on average, no greater than in non-resistant paddocks (Llewellyn and Powles, 2001). Thus the economic difference between the scenarios is not primarily due to differences in weed densities, but to differences in total treatment costs.

These economic differences are substantial. As selective herbicides become more available, the equivalent annual profit (annualised gross margin) increases from A$64 ha^-1 to A$93 ha^-1. The marginal value of an additional herbicide application reduces as total herbicide usage increases. For example, going from two to four applications increases profit by A$12 ha^-1 year^-1, while going from eight to 10 increases profit by only A$2 ha^-1 year^-1.

Underlying these results are biological simulation results for weed population dynamics and agricultural production over the 10-year period. Figure 2 illustrates the pattern of ryegrass density and enterprise gross margin (A$ ha^-1 year^-1 undiscounted) over the 10 year period. Weed numbers are consistently low, except in the final year where it is economic to slightly relax the level of weed control because subsequent years are not included in the economic calculation. To limit the allowed extent of relaxation, a constraint is imposed that final weed seed numbers must not exceed numbers at the start of the first period.

Figure 2. Annual gross margin ($ ha^-1) and weed density in crop before harvest (m^-2) over 10 years for lupins:wheat rotation with 10 applications of selective herbicide subject to treatments shown in Table 2.

For this rotation, gross margin oscillates from year to year, due to the low gross margin of the lupin crop. However, the gross margin in the wheat year is increased by the presence of lupins the previous years due to higher wheat yield and lower nitrogen fertilizer requirements. This is further apparent in the next set of results.

Interaction between rotation choice and weed management

Table 3 shows similar results to Table 2 but for a range of different crop and pasture rotation sequences. All are for the scenario of 10 uses of selective herbicides. The results for the WL (wheat-lupin) rotation are the same as the final column of Table 2. They are included here for easy comparison.

Table 3. Interaction between choice of crop:pasture rotation sequence and weed control practices over 10 years.

^AW = wheat, L = lupin crop, P = pasture (subterannean clover)

^BThe number of years in which this treatment was applied is shown in brackets.

The last rotation, which includes pasture phases, includes grazing as an additional weed control treatment. Sheep help to control ryegrass by grazing ryegrass seed over the summer months and grazing ryegrass plants in the pasture. The different pasture types result in different levels of ryegrass control. Subterranean clover pasture (selected here) gives the best weed control as the sheep are more easily able to select for ryegrass from among the pasture sward. RIM includes the option of high intensity grazing over the winter/spring period. The sheep are stocked at a sufficient intensity as to stop seed set. This option is economically preferred in each of the pasture years of the fourth rotation.

As Table 3 shows, the types of treatments selected for the first three cropping-only rotations are broadly similar, although high seeding rates are slightly less attractive in rotations with a greater frequency of wheat cropping. However, the rotation that includes pasture, pasture:pasture:wheat:lupins:wheat, is somewhat different in its mix of treatments. As well as high intensity grazing of the pastures (discussed above), paraquat is applied prior to weed seed set in the second of the two years of pasture. These treatments allow a lower reliance on seed catching, which is used only twice over the 10 years. Delayed seeding is used once.

Most strikingly, the inclusion of these pasture phases with these weed treatments makes it not merely feasible but economically optimal to use fewer applications of selective herbicide. They are not used at all in any of the four pasture years. This means that the reported equivalent annual profit for pasture understates the true economic value of the strategy, since the figure of A$70 ha^-1 year^-1 does not include a value for the four applications of herbicide which have been conserved for future use. Judging from Table 2, these four applications might be worth between A$10 and A$20 ha^-1 year^-1 (annualised) over the subsequent 10 years. Given the other assumptions about yields and sale prices underlying these runs, this extra value does not appear sufficient to make up the profit shortfall of the pasture rotation relative to the continuous cropping rotations.

Of the three cropping-only rotations, lupins:wheat:wheat and continuous wheat are similarly profitable. This reveals the contribution that lupins make to subsequent wheat profitability. In the lupins:wheat:wheat rotation, the contribution is sufficient to make up for the loss of income which occurs in the year of lupin production. Figures 3 and 4 further illustrate this. In Figure 3, the profitability of wheat is well above A$100 ha^-1 year^-1 in the wheat years, but low in lupin years. In Figure 4, apart from the first two years when weed density is relatively high, wheat gross margin is approximately A$100 ha^-1 year^-1. Average weed density is slightly lower in the lupin rotations that in the other two.

Figure 3. Annual gross margin ($ ha^-1) and weed density in crop before harvest (m^-2) over 10 years for lupins:wheat:wheat rotation with 10 applications of selective herbicide subject to treatments shown in Table 3.

Figure 4. Annual gross margin ($ ha^-1) and weed density in crop before harvest (m^-2) over 10 years for continuous wheat cropping with 10 applications of selective herbicide subject to treatments shown in Table 2.

Finally, Figure 5 shows the equivalent graph for the pasture:pasture:wheat:lupins:wheat rotation. Wheat is highly profitable each of the four times it is grown, as it follows a legume phase in each case. Weed numbers are relatively erratic, compared to other rotations, but little higher on average. The main reason for the lower profitability of this rotation is the current economic climate for livestock production, rather than its biological productively or effectiveness for weed management. A change in wool or meat markets could alter its economic performance. Nevertheless, the required increase in sheep profitability for the profitability of this rotation to match that of the most profitable cropping rotation is high. RIM shows that it would need to increase from A$11 head^-1 year^-1 to A$29 head^-1 year^-1. If an allowance of A$10 ha^-1 year^-1 is made for the average annual value of conserving four applications of selective herbicide, the required gross margin for sheep should be A$23 head^-1 year^-1, which is still a substantial increase.

Figure 5. Annual gross margin ($ ha^-1) and weed density in crop before harvest (m^-2) over 10 years for pasture:pasture:wheat:lupins:wheat rotation with 10 applications of selective herbicide subject to treatments shown in Table 3.

CONCLUSION

RIM provides a powerful tool for evaluating the biological, agricultural and economic performance of alternative long-term weed management systems. The model provides a comprehensive representation of the farming system at the single field level, complementing tools for whole-farm analysis by providing greater biological detail and a greater range of management options. It allows evaluation of important questions such as the following.

• How much income is likely to be lost by farmers once herbicide resistance develops?
• Which combination of strategies provides the best overall management system for a particular farming situation once resistance is present?
• Is it worth trying to delay the onset of resistance by using herbicides less frequently?
• Is it possible to maintain a continuous cropping rotation once resistance is present?
• If it is possible, is it economically sensible to maintain a continuous cropping rotation once resistance is present?
• If a pasture phase is included, how long should it be?
• Is a particular weed treatment a profitable practice? If so, in what circumstances?

In results presented it is shown that loss of herbicides due to herbicide resistance has severe economic ramifications in this farming system. As herbicides are progressively removed from the management system, a large number of alternative weed control practices are introduced. The economically preferred combination of these alternative practices is approximately as effective in weed control as the system including herbicides, but the cost of the herbicide-based system is substantially lower. Choice of crop:pasture rotational sequence is also shown to be an important tool for weed management. Inclusion of pasture phases has the potential to reduce reliance on herbicides, but suffers for poor economic returns given current market conditions.

ACKNOWLEDGMENTS

We are grateful for advice, information and assistance from a large number of people who contributed to the development of RIM. We particularly thank Amir Abadi, David Bowran, Art Diggle, Mike Ewing, Gurjeet Gill, John Holmes, John Matthews, David Morrison, Bill Roy, and Brian Trenbath. Institutional support for the model’s development and application was provided by the University of Western Australia, Agriculture Western Australia and the Grains Research and Development Corporation.

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Pannell, D.J., Stewart, V.A.M., Bennett, A.L., Monjardino, M., Schmidt, C.P., and Powles, S.B. (1999). RIM 99 User’s Manual, A Decision Tool for Integrated Management of Herbicide-Resistant Annual Ryegrass, University of Western Australia, Nedlands, Australia.

Powles, S.B. and Bowran. D.G. (2000). Crop Weed Management Systems. In: Australian Weed Management Systems. B. Sindel (ed.). R.G. & F.J. Richardson, Melbourne, pp. 287-306.

Powles, S.B. and Matthews, J.M. (1996). Integrated weed management for the control of herbicide resistant annual ryegrass (Lolium rigidum). In: H. Brown et al. (eds.), Second International Weed Control Congress. Department of Weed Control and Pesticide Ecology, Flakkebjerd, Copenhagen, pp. 407-413.

Preston, C. and Powles, S.B. (2000). Mechanisms of multiple herbicide resistance in Lolium rigidum. In: J.M. Clark (ed.), Pesticide Resistance 2000, American Chemical Society, (in press).

Robison, L.J. and Barry, P.J. (1996). Present Value Models and Investment Analysis, The Academic Page, Northport, Alabama.

Schmidt, C.P. and Pannell, D.J. (1996a). Economic issues in management of herbicide resistant weeds. Review of Marketing and Agricultural Economics 64, 301-8.

Schmidt, C.P. and Pannell, D.J. (1996b). The role of herbicide-resistant lupins in Western Australian agriculture. Crop Protection 15, 539-48.

Sindel, B. (ed.) (2000). Australian Weed Management Systems. R.G. & F.J. Richardson, Meredith, Victoria.

Stewart, V.A.M. (1993). Economics of integrated strategies for managing herbicide resistant ryegrass under continuous cropping. Unpublished Honours Thesis, Faculty of Agriculture, The University of Western Australia.

Tanji, A., Zimdahl, R. L., and Westra, P. (1997). The competitive ability of wheat (Triticum aestivum) compared to rigid ryegrass (Lolium rigidum) and cowcockle (Vaccaria hispanica). Weed Science 45, 481-487.

Walsh, M. J., Duane, R. D., and Powles, S. B. (2001). High frequency of chlorsulfuron resistant wild radish (Raphanus raphanistrum) populations across the Western Australian wheatbelt. Weed Technology 15, 199-203.

Citation: Pannell, D.J., Stewart, V., Bennett, A., Monjardino, M., Schmidt, C. and Powles, S. (2000). RIM: A Bioeconomic Model for Integrated Weed Management. (SEA Working Paper 00/10). http://www.general.uwa.edu.au/u/dpannell/dpap0010.htm

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Copyright © 2000 David J. Pannell, Vanessa Stewart, Anne Bennett, Marta Monjardino, Carmel Schmidt and Stephen Powles
Last revised: August 14, 2008.