SEA Working Paper 00/06
Adoption of integrated weed management to conserve the herbicide resource: review and framework
Rick Llewellynab, Bob Lindnera, David Pannella & Stephen Powlesb
aAgricultural
& Resource Economics
bWestern Australian Herbicide Resistance Initiative
Faculty of Agriculture, University of Western Australia
Abstract
The demonstrated ability of major cropping weeds to evolve resistance to most major herbicides threatens the sustainability of herbicide-dependent weed management systems. Although resistance to some herbicides is already widespread, most grain growers have several herbicide options still available to control weed infestations in crops. These growers are being encouraged to adopt integrated weed management practices that place less reliance on herbicides to delay, if not prevent, the emergence of further herbicide resistance. It is argued that extensive adoption of such practices will require a form of resource conservation decision, the resource being herbicide susceptibility. To maximise the net present value of returns, growers need to select the optimal use of herbicide susceptibility and the more costly non-herbicide practices over time. This paper integrates concepts of resource economics and the literature on the adoption of innovations to contribute to a framework for the adoption of integrated weed management practices where herbicide resistance is developing. Implications for achieving rapid and high level adoption by growers are discussed, given the requirement for perceived profitability in a complex adoption context where high uncertainty is expected.
Introduction
Since the early 1980s Australian grain growers have had a range of herbicide options available to them that have allowed selective removal of weeds from crops. This herbicide technology has facilitated major changes in farming practices. Trends associated with increased use of herbicides include more frequent cropping of arable land, the ability to grow less competitive crops such as pulses, reduced cultivation and reduced burning of crop residues. The latter two trends form part of the shift to what has been referred to as ‘conservation farming’, one aim of which is to reduce soil erosion risks. The resulting farming system is one that relies heavily on herbicidal weed control for both productivity and sustainable land management. In Western Australian, the average grain grower spends over $40 on sprays per hectare of crop (ABARE 1998).
The evolution of herbicide resistance threatens the sustainability of this farming system. The major weed in Australian cropping, annual ryegrass (Lolium rigidum) , has a demonstrated ability to evolve resistance to most of the major herbicide chemistries used for its control and is recognised as the most resistance-prone weed species world-wide . Herbicide resistance in other weed species is also increasing .
Growers are being encouraged to adopt weed management strategies that reduce the selection pressure for herbicide resistance with the aim of delaying, if not preventing, resistance development. Referred to as integrated weed management (IWM), this involves the adoption of a diverse range of weed control practices that reduce the reliance on herbicides. Since all growers still have several effective herbicide options available it is argued that extensive adoption of IWM practices will generally involve a decision on the optimal use of the herbicide resource. That is, growers face a question of how much of the stock of herbicide susceptibility to use now and how much should be conserved for the future. Improved understanding of the economic factors contributing to farmer decision making on resistance management should allow for more effective extension and information strategies to be developed.
Literature on the adoption of agricultural innovations and concepts of resource economics are integrated to produce a framework for better understanding the requirements for the adoption of IWM practices by growers where herbicide resistance is developing. The focus here is on the optimal use of herbicides.
The herbicide resource
The close relationship between the economics of pest resistance and resource economics was demonstrated by Hueth & Regev in a paper referring to insect management. They argued that whilst the pests themselves have been viewed (by economists at least) as a renewable resource, the effectiveness of chemical control is a potentially exhaustible resource that also requires management.
Using this approach, pest susceptibility is viewed as biological capital, a resource stock that can be managed similar to resource stocks in other extractive industries. Application of the pesticide, and the consequent selection for pest resistance, is then the form of extraction.
Even at this basic level, adapting this framework to herbicides and weed management requires some justification. Firstly, can herbicides be considered a potentially exhaustible resource? That is, is the rate of renewal of the herbicide resource likely to be exceeded by the rate of depletion?
Herbicide resistance development – the rate of depletion
The potential for depletion of the stock of herbicide susceptibility is well documented in Australia. The weed that is the focus of this paper, annual ryegrass (Lolium rigidum), is recognised as the most important weed of Australian cropping . It has a demonstrated ability to evolve resistance to all major selective herbicides available for its control . In several cropping areas the majority of paddocks contain a ryegrass population resistant to major herbicides . Cases of herbicide resistance in other major Australian cropping weed species are also rapidly increasing in frequency .
Previous studies of pesticide management as a resource management problem have focused on insects and insecticides. One reason for this is that, in the US at least, herbicide resistance development has been less significant than insecticide resistance development . The rapidly increasing herbicide resistance problem in Australia now presents a need to reassess herbicide use as a resource management problem.
New herbicide development – the rate of renewal
The development of effective new herbicide products is one avenue for renewal of herbicide susceptibility. Recent history and the trends outlined below suggest that this cannot be relied upon. All herbicides introduced to the Australian broadacre cropping market since the early 1980's have belonged to an existing herbicide group and as such have not had novel modes of action (Table 1).
Table 1. Development dates for herbicides classified by mode of action.
| MODE OF ACTION1 | HERBICIDE
EXAMPLE2 Active component Common trade name |
YEAR DEVELOPED3 | |
| A | Diclofop | (Hoegrass) | 1974 |
| B | Chlorsulfuron | (Glean) | 1979 |
| C | Simazine | (Gesatop) | 1955 |
| D | Trifluralin | (Treflan) | 1959 |
| E | Triallate | (Avadex) | 1960 |
| F | Amitrole | - | 1953 |
| G | Oxyfluorfen | (Goal) | 1975 |
| H | Thiobencarb | (Saturn) | 1969 |
| I | 2,4-D | - | 1945 |
| J | Flupropanate | (Frenock) | 1968 |
| K | Flamprop | (Mataven) | 1971 |
| L | Paraquat | (Gramoxone) | 1958 |
| M | Glyphosate | (Roundup) | 1971 |
| N | Glufosinate | (Liberty) | 1981 |
1Australian National Association for Crop Production and Animal Health (AVCARE) herbicide groups.
2Shading indicates commonly used herbicide group for control of grass weeds in southern Australian cropping.
3Dates from Herbicide Handbook.
World trends in the pesticide industry appear likely to result in fewer new herbicide developments. Increasing pesticide regulation costs result in fewer new pesticide registrations, particularly for minor crops . From a world perspective, many crops in southern Australian rotations, and the Australian herbicide market in general, are considered minor.
Another factor that can lead to renewal of herbicide susceptibility is regression of herbicide resistance. That is a situation where once the herbicide selection pressure is removed, the proportion of resistant plants in the population declines. One mechanism by which this might occur is through the resistant plants suffering a fitness penalty, as is the case with weeds resistant to triazine herbicides . In Australia, regression of resistance in our major weed, annual ryegrass, has not been documented. Studies investigating ryegrass with common forms of resistance have not found significant fitness penalties .
This overall combination of rapid development of herbicide resistance in Australian cropping and the uncertain prospects for renewal suggests growers are managing a potentially exhaustible resource. An important factor in how this should be managed by growers is whether or not the benefits from conservative herbicide use can be captured by individual growers.
The herbicide resource as private property versus open access
Much of the research into the economics of resistant pest management has focused on insects. The mobility of major insect pests has meant that insecticide susceptibility is often treated as an open access resource where collective pest-control actions may be required to achieve socially optimal pesticide use. Aggregate US data of insecticide sales has been used to demonstrate the open access characteristics of insect susceptibility .
Weeds exhibit some mobility through seed import and pollen flow carrying resistant genes. However, it is generally assumed that growers ‘raise and own’ their weed problem as a private property resource. If growers do not see resistance development as appropriable then behaviour associated with an open access resource would be expected. That is, the farmer has little incentive to act to prevent resistance developing, as resistance is likely to be introduced from other sources. For this reason, the possibility of growers perceiving significant levels of resistance gene immigration needs to be considered. As annual ryegrass is known to be an obligate outcrossing species, with resistance commonly a result of a dominant single-gene it is plausible that introduced resistant genes can be significant.
In the framework discussed here herbicide susceptibility is generally considered to be private property. However, in understanding growers’ herbicide use patterns the potential for perceived common property characteristics should not be overlooked.
A simple two-period model of optimal herbicide use
As previously discussed, growers are being encouraged by weed scientists and extension agents to pre-emptively reduce their use of herbicides in the short-term to allow the effective life of the available herbicides to be extended. From an economic perspective the primary objective would be to achieve the optimal herbicide use pattern that maximises the net present value of returns over time .
In the simple framework that follows (Figure 1), herbicide susceptibility is considered to be non-renewable and herbicide resistance is solely a function of the number of herbicide applications. Two periods of unspecified lengths are considered. Period 1 beginning with low resistance levels (e.g. ‘now’) and Period 2 is in ‘the future’ and dependent upon actions in Period 1 . Whilst containing major simplifications this example illustrates the essential concepts which are investigated in more detail later in this paper.
Figure 1. Optimal use of herbicide susceptibility over time showing marginal benefits and costs of herbicide use over two periods
Adapted from Miranowski & Carlson
The total stock of herbicide susceptibility is represented by 0-S along the x-axis. In the absence of Period 2 considerations, the optimal amount of herbicide use in Period 1 would be S1, where the marginal benefit of herbicide use in Period 1, MB1, is equal to the marginal cost of herbicide application, MC1. This would result in only S1-S of the total stock of susceptibility, 0-S, remaining available in Period 2.
Marginal benefits in Period 2 are represented by MB2 which runs in the reverse direction to MB1 as the remaining stock of susceptibility from Period 1 lies at the right of the x-axis. The marginal costs of herbicide application in Period 2, MC2, are lower than MC1 to allow for future discounting. MB2 can also be considered to be translated into present values. If Period 2 is considered on its own, or if there was no resistance development, S2 would be the optimal amount of herbicide use in Period 2. However, if S1 is used in Period 1, and resistance is developing, then the amount of susceptibility S-S2 would clearly be unavailable in Period 2.
To allow for intertemporal allocation, the cost of foregone future net benefits through the use of herbicide in Period 1 is represented by the marginal user cost, MUC. These user costs arise as a result of resistance development and the subsequent yield loss caused by higher weed levels and/or the higher cost of alternative weed control practices. This is based on the assumption that in any single year an effective herbicide is the most cost-effective method of achieving a high level of weed control.
When MUC is considered, the marginal cost of herbicide use in Period 1 becomes MC*, the sum of MUC and MC1. This gives the optimal herbicide use in Period 1 of S* where MB1=MC*. This allocation uses 0-S* of the stock of susceptibility in Period 1, leaving S-S* available in Period 2.
We are now able to make use of this framework in interpreting some of the factors that may be influencing herbicide use decisions. If, for example, the use of herbicide in Period 1 is not believed to lead to resistance development then the farmer’s herbicide use in Period 1 will be S1 as no user costs will be assumed. Similarly, if the farmer believes that, regardless of his/her own actions, resistance will still be introduced (e.g. through pollen or seed introductions) then the farmer will have no incentive to conserve susceptibility in Period 1 as the MUC would be zero. The cost of resistance build-up will be ignored, and S1 will again be used in Period 1.
Alternatively, if MUC is considered in Period 1 but its value is underestimated then herbicide use in Period 1 will be above optimal levels. This could occur if the rate of resistance development is not understood or the cost of managing a herbicide resistant weed population is underestimated.
The rate of future discounting of Period 2 marginal cost and benefits affects S*. The higher the rate of discounting, the greater the use of herbicide in Period 1 as S* shifts to the right.
Weed management and herbicide resistance in cropping -a simple model
To further explore the concepts and simplified functional forms represented in the above two-period model it is necessary to examine some of the bioeconomic relationships in more detail. To do this, a situation where a farmer is continuously cropping and has the option of herbicide weed control and a range of non-herbicide weed control practices is assumed.
Let
p t = profit in year t
P = price per unit yield
Y = crop yield
CN = cost of a unit of non-herbicide weed control
CH = cost of herbicide treatment
CF = costs associated with growing the crop which exclude weed control. These costs include seeding, fertiliser and harvesting costs and are considered fixed.
Ht = a binary variable: 1 if you apply the herbicide, 0 if not.
Nt = a number representing intensity of use (number of units) of non-herbicide treatments.
p t = P.Yt – CH .Ht – CN .Nt - CF (1)
Similar to the optimal control model developed by Gorddard et al , only the costs of the decision variables, herbicide use and use of non-herbicide practices, are considered variable.
Yield is a function of total weed density surviving treatments in the current period.
Yt = Y(Wt) (2)
We know that dY/dW <0, and that d2Y/dW2 >0. This is recognised as the most common form of the relationship between crop yield and weed density .
Weed density is given by
Wt = g .Wt-1.[1 - kH(Ht)].[1 - kN(Nt)] (3)
where
It is assumed that no individual non-chemical treatment is able to achieve the high proportion of weed kill that herbicides can achieve. As such, multiple non-chemical treatments (i.e. higher N) are required to achieve high levels of kN (see Figure 2). In an example where there are four non-herbicide practices available and each is able to provide 50% control, the use of one practice (N = 1) achieves kN = 0.5, but when all four practices are used (N = 4), the overall level of control is 0.9375. This is equivalent to the effectiveness of a single application of some herbicides. Depending on the cost of the non-herbicide practices (CN), this relationship can result in very high weed control costs when non-herbicide practices alone must be relied upon for high levels of weed control.
Figure 2. A suggested relationship between the number of non-herbicide practices used in a year (N) and the proportion of weeds killed by non-herbicide practices (kN)
This relationship would be expected to affect the curve MUC and hence MC* in Figure 1 such that it would cause increasing marginal costs as herbicide use increases.
The proportion of weeds killed by the herbicide, kH , depends on resistance status, Rt (the % of weeds that are resistant) prior to herbicide treatment in year t. Specifically,
kH = kS(Ht).(1 - Rt) (4)
where kS is the kill function for susceptible weeds. If Ht = 1, kS is some high proportion, such as 0.95. If Ht = 0, kS = 0.
Resistance status in the current period depends on resistance status in the previous period and herbicide usage in the previous period.
Rt = g(Rt-1, Ht-1) (5)
Specifically, if Ht = 0, Rt = Rt-1 and if Ht = 1, Rt = Rt-1 + f where f is some increase in resistance that would be a function of Rt-1.
The suggested relationship between Rt and cumulative applications of herbicide (H) shows that Rt is slowly increasing for each early application of H before, briefly, becoming rapidly increasing (Figure 3).
Figure 3. A suggested relationship between the proportion of herbicide resistant plants, Rt , and cumulative applications of herbicide (H).
Given the relationship shown in Figure 3, which is consistent with several genetic models of resistance build-up , increases in the proportion of resistant plants, f , remain low whilst Rt is low. This produces the relationship shown in Figure 4 where once Rt-1 reaches a certain level, another herbicide application results in a very large increase in resistance, f . It is for this reason that herbicide effectiveness is often referred to as being limited to a number of ‘shots’, after which the herbicide is ineffective. This relationship means that the marginal benefits from herbicide use decreases rapidly once resistance starts to develop. The pattern of resistance development also makes observation of Rt-1 very difficult during the early stages of resistance development.
Figure 4. A suggested relationship between the increase in the proportion of resistant plants, f , when a herbicide is applied to a population with the proportion of resistant plants Rt-1.
Finally,
NPV = S t=1..n p t b t (6)
The decision problem is to select H and N in each year of each period to maximise NPV from the time t=1 to the time horizon t=n, where b is the discount factor 1/(1 + r)t-1 and r is the discount rate.
Two-Period Example
Referring again to a two-period problem, we can use the model above to help identify the key factors determining the net benefits of choosing the conservative herbicide use pattern, S*, compared to the myopic S1 herbicide use pattern (from Figure 1). In this, the simplest of examples, S* involves not using a herbicide in the first year, Period 1 (i.e. H1=0), and conserving susceptibility for the second year, Period 2. The S1 herbicide use pattern involves using the herbicide in Period 1 (i.e. H1=1) leaving reduced susceptibility in Period 2.
First considering the net gain in profit in Period 1 if the herbicide is used (as it would be using the herbicide use pattern S1):
p 1|H1=1- p 1|H1=0 = P(Y1|H1=1 - Y1|H1=0) – CH – CN(N*1|H1=1 – N*1|H1=0) (7)
where N* is the optimal level of non-herbicide treatment.
In Period 2 the interest lies in how herbicide use in Period 1 affects profits in Period 2:
p 2|H1=1 - p 2|H1=0 = (P(Y2|H1=1 - Y2|H1=0)–CH (H*2|H1=1 – H*2|H1=0) – CN(N*2|H1=1 – N*2|H1=0) (8)
where H*2 is the optimal choice of whether to use or not use herbicide in Period 2.
Over the two periods, the choice of S* over S1 requires that the discounted gains in Period 2 from not using herbicide in Period 1 are greater than the Period 1 losses.
(p 2|H1=0 - p 2|H1=1)b > (p 1|H1=1 - p 1|H1=0) (9)
It would be expected that the greater the difference the more likely it would be that growers adopt the reduced level of herbicide use in Period 1 (S*), and therefore greater pre-emptive use of integrated weed management practices. Relating this to growers’ perceptions, adoption of IWM practices in Period 1 would be more likely if:
p 2|H1=0 - p 2|H1=1 is perceived to be relatively high. This would be the case if no herbicide use in Period 1 resulted in:
p 1|H1=1 - p 1|H1=0 is perceived to be relatively low. This would be the case if no herbicide use in Period 1 resulted in:
Overall, the impact on profit in Period 2 depends on:
(g .Wt-1).
A factor which appears to have a conflicting interaction with the profitability of conserving herbicide use is the cost of non-herbicide practices (CN). This is due to the same non-herbicide methods which allow herbicide use to be conserved also being the likely methods which allow for control of weeds once resistance has developed. Therefore if CN is lower the relative cost of pre-emptive IWM adoption (p 1|H1=1 - p 1|H1=0) will be reduced if herbicides can be cheaply substituted with non-herbicide methods. However, lower CN is also likely to reduce the cost of managing a resistant population and hence the relative value of conserved herbicide susceptibility (p 2|H1=0 - p 2|H1=1). The overall impact of CN is likely to require consideration of the relationship shown in Figure 2, where higher levels of weed control using just non-herbicide methods is shown to involve rapidly increasing costs. This example demonstrates one of the complexities in achieving perceptions of profitability of pre-emptive adoption and the challenges for extension.
Perceptions of profitability in conserving herbicide susceptibility
The framework allows for some explanation of how adoption may or may not be profitable for individual growers. From the pioneering diffusion studies of Griliches to more recent literature it is evident that the profitability of an innovation can explain much of the variation in the adoption decision.
However, given that adoption can essentially be viewed as a process involving uncertainty and learning , it is growers’ perceptions of profitability that are likely to be of most relevance. Unless there is complete knowledge about the innovation, which is certainly not the case for herbicide resistance and IWM, growers’ perceptions can explain much of the observable differences in adoption .
So what are the difficulties in developing accurate perceptions of the profitability of conserving herbicide susceptibility? The process is made difficult by the fact that it is essentially a conservation, or preventative, ‘innovation’. As demonstrated in the models above, this infers that some short-term profits may need to be foregone to minimise a decline in returns in some future period. Innovations such as these are recognised as having particularly slow rates of adoption . One of the explanations for this is high uncertainty and that is what will be focused on here.
Factors Contributing to High Uncertainty
The extended time frame for returns from adoption increases uncertainty . Growers are faced with considerable uncertainty regarding factors such as the rate of resistance build-up, the cost of controlling weed populations without the use of herbicides and the future availability of new weed control methods. These add to the standard elements of uncertainty associated with farming such as commodity prices. Recent literature suggests an even more important role for uncertainty. Dong and Saha argue that even if the returns from adopting are expected to be positive, adoption may still not occur as the returns from waiting for further reductions in uncertainty may be higher.
Appropriate information can reduce some uncertainty. However, for the herbicide resistance/IWM problem attaining high quality information can be difficult, as well as costly. Two major attributes identified by Rogers as determining the rate of adoption, observability and trialability, are not well satisfied. The development of resistance is not often observable until the effectiveness of the herbicide is almost lost. As a result, the ability to observe the effect of reduced herbicide applications on the stock of weed susceptibility is made difficult. Similarly, this affects trialability. Whilst individual IWM practices may be able to be trialed and observed to varying degrees, their impact in the context of conserving herbicide susceptibility is not so readily observable.
There is also the potential for considerable uncertainty to be associated with the ongoing importance of herbicide resistance development. Due to the competitive commercial nature of pesticide development little is publicly known about the probability of new herbicide developments. The potential for new herbicide groups that will reduce the impact of current forms of resistance is highly uncertain. Similarly, there is uncertainty regarding the development of new non-herbicide weed control technology or the future profitability of enterprises which demand less herbicide use (e.g. grazing).
Although not discussed in any detail here, the IWM practices themselves present their own set of impediments. The importance of perceptions of not just the ‘problem’ but of technology-specific attributes has been recognised in recent studies . IWM, by definition, involves a range of practices and therefore a large number of technology-specific attributes. As suggested in this paper, cropping without herbicides is likely to require several weed control practices used in conjunction. This complexity adds to the potential for misperceptions and high uncertainty.
The role of information and extension in the pre-emptive adoption of IWM practices
Even if pre-emptive adoption of IWM practices is actually profitable, the adoption scenario is clearly complex and as such rapid adoption is difficult to achieve. Aside from developing new weed management practices, those with an objective of increasing pre-emptive IWM adoption essentially have the provision of information as the main tool. Where the pest being considered has very low mobility there is little justification for policy other than that which overcomes a lack of information . Improved knowledge and better informed decision making then becomes the objective. Extension of herbicide resistance and IWM information can achieve this by reducing uncertainty and overcoming misperceptions. If the argument that the described adoption scenario is one involving particularly high levels of uncertainty is correct, then it would follow that the potential impact of information and facilitated learning is also high.
The framework in this paper is being used as a basis for an empirical study using data from interviews with individual growers in the Western Australian wheatbelt. A major objective of this work is to test whether grower perceptions and adoption behaviour are consistent with a private property, exhaustible resource model and to identify important factors influencing the adoption decision. The role of information in influencing perceptions shown to be important in the adoption decision, including possible misperceptions and perceptions of uncertainty, will be examined and tested.
Conclusion
A number of organisations involved with crop production have an objective of increasing pre-emptive adoption of IWM practices by growers. What has been presented in this paper suggests that there are major challenges in achieving this. A framework for understanding the important factors determining profitability, together with the likely role of high uncertainty, has been presented. Given the current extent of herbicide resistance in Australian cropping and the demonstrated potential for this to increase it is suggested that a framework that considers herbicide susceptibility to be a potentially exhaustible resource may be appropriate. Growers must then choose the optimal levels of herbicide and integrated weed management practice use over time, in an adoption scenario where uncertainty is expected to be high. Gaining a greater understanding of the rational economic basis for growers’ herbicide resistance management decisions should assist in targeting research and extension delivery. The aim being optimal use of the herbicide resource over time.
Acknowledgements
The Grains Research and Development Corporation and the CRC for Weed Management Systems provide financial support for this research.
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Citation: Llewellyn, R., Lindner, R.K., Pannell, D.J. & Powles, S. (2000). Adoption of integrated weed management to conserve the herbicide resource: review and framework, Australian Agribusiness Review http://www.agribusiness.asn.au/review/2001v9/Llewellyn_2001_1/Llewellyn.htm (SEA Working Paper 00/06, Agricultural and Resource Economics, University of Western Australia).
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