Integrated joint pest management strategies in the presence of control spillovers

Transcription

1 European Review of Agricultural Economics Vol 40 (5) (2013) pp doi: /erae/jbs044 Advance Access Publication 11 January 2013 Integrated joint pest management strategies in the presence of control spillovers Yigezu A. Yigezu, Corinne E. Alexander *, Paul V. Preckel, Dirk E. Maier, Linda J. Mason, Charles P. Woloshuk, John Lawrence and Dale J. Moog International Center for Agricultural Research in the Dry Areas (ICARDA), Amman, Jordan; Purdue University, West Lafayette, IN, USA; Kansas State University, Manhattan, KS, USA Received September 2010; final version accepted October 2012 Review coordinated by Paolo Sckokai Abstract Integrated pest management has proved cost-effective in coping with crop pests. This article identifies characteristics of pests, controls and economic incentives that may make the development of an integrated joint pest management (IJPM) strategy designed for simultaneous control of multiple pests worthwhile. We demonstrate via a case study that an IJPM strategy may add considerable value for on-farm corn storage. Critical factors for an IJPM strategy are that the pests are affected by the same environmental variables, but with different thresholds and response functions; controls for one pest impact other pests; and the pests have similar economic importance. Keywords: integrated pest management, stochastic dynamic programming, insect management, mould management, stored corn JEL classification: Q12, Q16 1. Introduction Pest damage poses a major challenge for stored corn especially during warm, humid months. Pests in stored grain affect producers in two ways. First, the presence of live insects and the number of mould- and insect-damaged kernels beyond certain levels lead to price penalties or rejection by buyers. Second, dry matter, i.e. quantity, is lost due to respiration by mould and consumption by insect larvae. In the United States, annual post-harvest losses due to insects and moulds in corn and wheat are estimated at USD 1.5 to USD 2.7 billion (USDA, 2005; Yigezu et al., 2008). *Corresponding author: Department of Agricultural Economics, Purdue University, 403 West State Street, West Lafayette, IN 47907, USA. cealexan@purdue.edu # Oxford University Press and Foundation for the European Review of Agricultural Economics 2013; all rights reserved. For permissions, please journals.permissions@oup.com

2 786 Y. A. Yigezu et al. Owing to growing consumer concerns about food quality and the risk of pesticide exposure (Florax, Travisi and Nijkamp, 2005; Magnusson and Cranfield, 2005), integrated pest management (IPM) strategies for stored product rather than chemical-only-based strategies are of growing interest (Su et al., 2011). Responding to consumer trends, food processors reward producers for high-quality grain and encourage reduced pesticide use. Processors require that grain is stored on-farm and delivered throughout the year, which means storing on-farm longer than is traditional, increasing the difficulty of maintaining high quality. Processors may offer a premium price, assess penalties for grain quality deficiencies and provide a storage fee as an incentive for longer storage. Producers may increase their ability to deliver high-quality grain by using IPM strategies. The key feature of a storedgrain IPM strategy is that conditions in the grain bin are monitored to make subsequent management decisions (Phillips, Noyes and Adam, 2002). Although IPM has proved cost-effective for managing crop pests, in this article we identify characteristics of pests, controls and economic incentives that may make the development of an integrated joint pest management (IJPM) strategy one designed for the simultaneous control of multiple pests worthwhile. We demonstrate via a case study that an IJPM strategy may add considerable value for on-farm corn storage. Critical factors that suggest the need for an IJPM strategy are that the pests are affected by the same environmental variables, but with different thresholds and response functions; controls for one pest have an impact on more than one pest; and the pests are of similar economic importance. We demonstrate the value of an IJPM strategy for the management of moulds and insects in stored corn from the perspective of a producer maximising profits and the perspective of food processors designing contracts to source high-quality grain. Both moulds and insects are affected by the temperature of the stored grain. Because temperature is stochastic due to weather, the management strategy must take this randomness into account. Management strategies considered are aeration (both unconditional and conditional on ambient temperature relative to in-bin temperature), fumigation (which impacts insects but not mould) and sale (which arrests the economic consequences of damage). Aeration impacts both moulds and insects primarily through altering grain temperature. Both pests contribute to value loss through the accumulation of damaged kernels. The economic impact of damage from both pests is of similar magnitude with complete value loss possible in extreme cases. 2. Literature review The majority of the literature on the economics of IPM has focused on managing field pests, commencing with the theoretical work by Hall and Norgaard (1973). One contribution of economists to the IPM literature was to determine the optimal management strategies for control of pests, using dynamic programming (Zacharias and Grube, 1986; Swinton and King, 1994; Forcella et al., 1996) or simulation (Greene et al., 1985; Hyde et al., 1999; Monjardino,

3 Integrated joint pest management strategies 787 Pannell and Powles, 2004, 2005). However, IPM is complex and knowledgeintensive, creating barriers to adoption (McNamara, Wetzstein and Douce, 1991; Cowan and Gunby, 1996). More recently, researchers have examined whether extension efforts to promote IPM result in further adoption (Ricker- Gilbert et al., 2008; Rejesus et al., 2009). Researchers have also started examining the environmental and health impacts of IPM (Mullen, Norton and Reaves, 1997; Cuyno, Norton and Rola, 2001; Maumbe and Swinton, 2003; Mullen et al., 2005). Literature on the economics of IPM for stored products is sparse and focuses on developing optimal strategies, often for a single control, or a few combinations of strategies with fixed input intensities and application times. Anderson et al. (1990) used second-degree stochastic dominance to compare three different insect management strategies in stored wheat. Rulon, Maier and Boehlje (1999) and Adam et al. (2004) used cost benefit analysis to compare the profitability of individual insect management strategies such as fumigation and aeration. Fox and Hennessy (1999) determined the number of interventions of an individual pest control strategy to minimise economic loss during storage applied to the case of fumigation to control the lesser grain borer in wheat. Adam et al. (2010) showed that the value of a sampling-based IPM strategy for an elevator storing wheat depends on climate and the rate insects immigrate into the bins. While IPM can enable producers to reduce pesticide use and economically control pest damage, there are cases when the best approach is doing nothing or using a calendarbased fumigation strategy. Yigezu et al. (2008, 2010) considered multiple interventions for single-pest problems, using dynamic programming to determine the optimal intervention strategies for controlling moulds and insects, respectively. Following Yigezu et al. (2008, 2010), this article explicitly recognises that the timing-of-sales is an important pest management intervention for stored products. As noted by Fox and Hennessy (1999), damage in stored grains accumulates and cannot be reversed. Thus, if monitoring indicates that insect or mould damage is approaching high-consequence economic thresholds, further accumulation of damage can be avoided by selling immediately. Fox and Hennessy (1999) assume that storage period length is determined solely by futures prices and storage costs, ignoring the current level of damage. Adam et al. (2004) focus on aeration and fumigation as insect control strategies and do not consider timing-of-sales. This article is the first to model the economics of stored product IPM simultaneously considering multiple pests and interventions. For the case of field crop pests, three other papers have examined the case of multiple pests and interventions (Zacharias and Grube, 1986; Monjardino, Pannell and Powles, 2004, 2005). We apply our approach to the case of corn stored on an Indiana farm (United States). An important feature is that interventions generally affect multiple pests, resulting in spillovers when a control applied for one pest affects another. Given these spillovers, the optimal IPM strategy is difficult to construct from the optimal strategies for individual pests. Thus,

4 788 Y. A. Yigezu et al. with spillovers, the multi-pest multi-control problem must be addressed in a single analysis i.e. there is a need to develop an IJPM strategy. 3. The bio-economic model The analysis reflects the underlying biology of insect and mould growth as it is affected by the physical environment and control actions combined with accounting relationships that track monitoring and pest control costs, and revenues from grain sales. The time horizon is a single storage season beginning at harvest and ending when bins are emptied in anticipation of the next crop. Producers monitor their bins every two weeks and make pest management decisions based on the physical condition of the grain. The storage period (16 October 31 July) is modelled as 19 periods of approximately two weeks each. 1 The system is viewed as a Markov chain with information regarding the past evolution of the system captured by its current state. Temperature is critical to both insect and mould growth, and the stochastic nature of temperature makes pest management challenging. The state of the system is jointly defined by four variables: the in-bin temperature (3 388C in 58C steps), the cumulative number of mould-damaged kernels (MDK) (0 21 per cent in steps of per cent), the cumulative number of insect-damaged kernels (IDK) (between and per cent, increasing in a geometric progression defined by IDK(i + 1) ¼ IDK(i), where i ¼ 1,..., 60 indexes the grid points and IDK(1) ¼ ), and whether the grain has been sold. Ideally, we would also include a state variable for the number of live insects. However, to limit computing requirements, we omit this state variable, tracking it indirectly based on the cumulative number of IDK. Using IDK as a proxy for the number of live insects is justified by three factors: (i) insect damage is only observable when an insect emerges from the kernel, meaning each live insect represents one IDK; (ii) we assume no insects die due to temperature extremes (which are extremely rare over the range of historical temperatures observed); and (iii) we assume, based on Yigezu et al. (2010), that fumigation is only optimal immediately before grain sale. The grain sale decision is an all-or-none decision to sell either to the food processor or to the elevator, or keep the grain for an additional period. The processor s base price is typically higher than the commodity price, and producers contract with processors to increase revenues. If the producer fails to meet the quality specifications, he can sell to grain elevators, 2 traditionally the first handlers of commodity corn. Because the processor pays a storage fee and elevator prices generally exhibit an upward trend through mid-march, 1 These periods are (i) October, (ii) 1 15 November, (iii) November, (iv) 1 15 December, (v) December, (vi) 1 15 January, (vii) January, (viii) 1 15 February, (ix) February, (x) 1 15 March, (xi) March, (xii) 1 15 April, (xiii) April, (xiv) 1 15 May, (xv) May, (xvi) 1 15 June, (xvii) June, (xviii) 1 15 July and (xix) July. 2 A grain elevator is a buyer with facilities that require an elevator system to lift the grain into the storage structure.

5 Integrated joint pest management strategies 789 keeping the grain after harvest is desirable. However, pest damage accumulates over time, leading to quantity losses and possible discounts or rejection. Producers trade off the benefits from higher future grain prices versus the risk of discounts or rejection. Aeration can forestall pest damage, and can be applied in a given period either unconditionally or conditional on temperatures. Unconditional aeration means the producer runs the fans for the entire two-week period. Conditional aeration means the producer only turns on the fans when the ambient temperature is at least three degrees below the in-bin temperature. Thus, producers trade off the aeration benefits of reduced pest damage versus the cost of running fans. 4. The stochastic dynamic programme The optimal strategy for pest management is calculated using stochastic dynamic programming. The objective is to maximise expected revenue net of storage costs. The state of the system is described by four components of the vector of state variables: in-bin temperature, number of cumulative MDK, number of cumulative IDK and whether the grain has been sold. The model is solved using backward recursion (Bellman, 1957; Bellman and Dreyfus, 1962), to determine the optimal value function in each time period given the state in that time period. The calculation of the optimal value function for a given time period is achieved by optimising for each state the current contribution to net returns plus the expected optimal value of future contributions to net returns with respect to the current period variables: whether and to whom to sell the grain, whether and how (conditionally or unconditionally) to aerate and whether to fumigate. The recurrence relationship (Bellman s equation) used for calculating the optimal strategy is: V ti = Max {p ti (S ti, A ti, F ti )+ae ijt [V t+1,j S ti, A ti, F ti ]} S ti,a ti,f ti [ ] = Max S ti,a ti,f ti p ti (S ti, A ti, F ti )+a j P tij (S ti, A ti, F ti ) V t+1,j, t, i, (1) where the notation is defined in Table 1. To initialise the recursion, the value function must be set for the final period for every state i. If the grain has not been sold by period T, the value function for that terminal period, V Ti, is set to the maximum revenue (adjusted for any losses of quantity or quality) from the processor or the elevator less costs. Given the state of the system, this procedure implements the optimal strategy sell the grain at the highest available price unless it was already sold. The relationship in equation (1) is then used to determine the optimal value function V T21,i for all states i. The relationship is applied again to obtain V T22,i, V T23,i, etc. until period 1 is reached. This recursive calculation was

6 790 Y. A. Yigezu et al. Table 1. Model parameters Parameters Definition T Time period I State of the system in period t J Potential states of the system in period t + 1 V ti Optimal expected value in period t and state i given that the optimal strategy is pursued beyond period t S ti Sales decision (to food processor, to elevator or wait) in period t and state i A ti Aeration decision (conditional, unconditional or do not aerate) in period t and state i F ti Fumigation decision (fumigate or do not fumigate) in period t and state i A Per period discount factor (calculated based on an 8 per cent discount rate and 24 equal periods per year even though periods vary between 13 and 16 days) p ti () Current period contribution to profit as a function of current period decisions in period t and state i (equal to the revenue from sales either to the food processor or elevator less the sum of monitoring and fumigation costs or aeration if those strategies are employed, unless grain has already been sold) P tij () Probability of transitioning from state i in period t to state j in period t + 1 given applied controls E ijt [. S ti, A ti, F ti ] Mathematical expectation operator with respect to state j, given state i in period t and actions S ti, A ti and F ti implemented using the General Algebraic Modeling System (GAMS) software (Brooke et al., 2005) Data Data to construct the state transition matrices were generated from PHAST- FEM 4 (Montross, Maier and Haghighi, 2002), an engineering simulation model. PHAST-FEM predicts mould and insect population growth based on initial in-bin conditions, applied controls and ambient weather. We use observations of ambient temperature, relative humidity, wind speed and solar radiation for Evansville, IN (United States) from the National Solar Radiation Data Base to reflect weather variability in PHAST-FEM. Weather data for 45 years are used to generate transition probabilities for in-bin temperature from one period to the next. 5 3 While the authors chose to implement the analysis using GAMS, other languages such as MATLAB or FORTRAN could have been used. 4 PHAST-FEM stands for Post-Harvest Aeration and Storage Simulation Tool-Finite Element Model. 5 This treatment of the transition probabilities ignores the possibility of a global climate trend. Future work could assess the impact of climate change on the optimal IJPM strategy.

7 Integrated joint pest management strategies 791 To generate the transition matrices for IDK and MDK, we exogenously set the state variables for each time period and use the PHAST-FEM simulation for alternative weather scenarios to generate the frequency distributions of one-period state transitions given the applied controls. The joint state transition probabilities for in-bin temperature and cumulative MDK are based on a total of 7,560 runs of PHAST-FEM for 45 years of weather data, three different aeration strategies, eight initial in-bin temperature levels and seven groups of periods (Yigezu et al., 2008). Joint state transition probabilities for in-bin temperature and cumulative IDK are based on 1,056 full-year runs of PHAST- FEM. The simulation results are used to estimate random effects regression models of the relationship between temperature and insect growth separately for 16 October 15 May, 16 May 30 June and 1 July 31 July. The estimated relationships are combined with discrete approximations to the distributions of the residuals to develop the conditional state transition probabilities (Yigezu et al., 2010). We use these conditional probabilities to generate the joint state transition probabilities for in-bin temperature, MDK and IDK assuming that mould and insect populations do not interact. PHAST-FEM makes the same assumption because there are no published estimates of the magnitudes of such interactions. These transition probabilities are estimated separately for the aeration controls, and hence the controls impacts are directly reflected through the probabilities of transitioning to alternative states next period. Fumigation zeros out the number of live insects. A key motivation for grain storage is that revenues rise over time for fixed grain quality and quantity. Typically, producers with food processor contracts commit to deliver high-quality grain at a specific time. However, the processor may allow early delivery if pest problems develop. Thus, the contracted delivery date is treated as 31 July, but the producer is allowed to deliver early as a pest management strategy. The price paid by the processor has several components. The base price is a 10-year average Chicago Board of Trade futures price from 1994/ /2006, with the drought years 1995/ 1996 and 2003/2004 dropped because they had atypical price patterns. Processor contracts allow producers to establish their base selling prices, using several different futures contracts, including the March, May or July contracts. We assume that the only futures price available is the contract with the closest settlement date. These futures prices are smoothed to eliminate price discontinuities caused by a switch in date for the contract with the closest settlement date. The processor pays a premium of USD 0.55 per bushel 6 plus a storage payment of USD 0.03 per bushel per month. The processor discounts this price by USD 0.01 per bushel for every percentage point of damaged kernels in excess of 3 per cent, where the total damage is assumed to be the sum of IDK and MDK. In extreme cases when the grain contains two or more live insects per kilogram or more than 6 per cent total damaged kernels, the processor rejects the grain, and the producer sells to the elevator at the current cash price. Producers without processor contracts sell their grain 6 A bushel is a volumetric measure equivalent to kg.

8 792 Y. A. Yigezu et al. to elevators at the cash price. Cash prices over the storage period are the 10-year average cash prices for Evansville, IN. Cash prices offered by the elevator are not smoothed, and the producer sells to the elevator only if their grain has been rejected by the processor. Grain sales to the elevator face price discounts of USD 0.01 per bushel for every percentage point of total damaged kernels over 6 per cent with a 20 per cent maximum. Producers control storage pests by aerating the grain to cool it to levels that inhibit pest growth (4 78C). The optimal time to sell grain for contract producers is determined by the tradeoffs between the contract premium for highquality grain and the risk of large penalties due to live insects and/or a high proportion of damaged kernels. For producers without a contract, the optimal timing of grain sales is determined by the tradeoffs between higher future prices and the risk of grain quality and weight losses due to pests during storage. We compare the IJPM management strategy with the benchmark of traditional practice identified through producer interviews (Yigezu et al., 2008). Traditional practice starts with drying the grain to per cent moisture content. Producers usually aerate in the fall until in-bin temperature is about 4 58C, but do not aerate during the winter or summer. They do minimal monitoring, involving visual and smell tests by opening the bin hatch. If they suspect pest activity, producers walk on the grain surface to check for pest damage. If there are mould crusts or hot spots due to high mould activity, they scoop these spots out of the bin or spread them across the grain surface. If mould infection is visible and substantial, they may aerate. The storage unit and the price data used in the model are adapted from Yigezu et al. (2008, 2010) (Table 2). We adjusted monitoring costs for this article. Labour for collecting grain samples and temperature data constitutes the major portion of the variable monitoring cost. This cost remains the same whether monitoring is for one or more pests. Hence, the time required for simultaneously monitoring moulds and insects is assumed to be 50 per cent higher than for insects alone (Yigezu et al., 2010). 5. Results Model results indicate gains to an IJPM strategy and highlight the importance of modelling timing-of-sales as a control strategy. They also specify the optimal strategy for applying control measures. Circumstances where the development of a joint strategy is likely to be advantageous are described Returns to IJPM Typical Indiana producers aerate the grain unconditionally in the fall until its temperature is approximately 58C and sell to an elevator on average around mid-march. Omitting the possibility of selling to the food processor, an option that is not available to most Indiana producers, our model indicates this timing of sales as optimal. However, model results indicate that the optimal aeration strategy is conditional aeration until the grain is sold.

9 Integrated joint pest management strategies 793 Table 2. Parameters used in the stochastic dynamic programme Parameter Units Parameter value Fixed costs Three HOBO w temperature data loggers per bin USD/bin temperature sensors per bin USD/bin 500 Five 6-inch pipes USD/bin 50 Five pitfall traps USD/bin 50 CO 2 sensor (one per bin) USD/bin 465 Drying cost per point per bushel USD 0.02 Variable costs Two flight pheromone traps (only after 1 April) USD/bin/period 15 Electricity cost USD/kWh 0.07 Fumigation cost a USD/bushel 0.18 Time required for insect monitoring h/bushel/round Other parameters Moisture content at harvest Per cent 22 Wage rate USD/h 10 Average bin size Bushel 36,000 Food processor premium b USD/bushel 0.55 Storage fee per bushel per month b USD 0.03 Interest rate Per cent/year 8 Penalty for damaged kernels in excess of the 3 and 6 per cent thresholds for food processors and elevators, respectively 6.2. Optimal pest management strategy USD/per cent point/bushel Figures 1 3 illustrate the optimal pest management strategy for three periods during which there are major shifts in strategy: 16 March, 16 June and 1 July. The control strategy depends upon the state variables MDK, IDK and in-bin temperature. The vertical axes indicate in-bin temperature, the horizontal axes indicate IDK and each panel corresponds to a different level of MDK. The low level of MDK associated with the top (A) panels in Figures 1 3 is zero. The high level of MDK associated with the bottom (C) panels is 5.5 per cent, near the 6 per cent threshold that triggers rejection by the processor. The average level of MDK associated with the middle (B) panels is the average-simulated MDK levels given the assumed initial conditions at the beginning of the storage season (2.3 per cent for 16 March, 4.0 per cent for 16 June and 4.5 per cent for 1 July). Regions in Figures 1 3 are labelled with acronyms indicating the optimal aeration and sales strategies, the expected date of sale, the buyer and whether fumigation will be needed, conditional on the cumulative number of MDK, IDK and the in-bin temperature. For example, in Panel B of Figure 1, when 0.01 a The estimated fumigation cost was obtained from a private company in the study area. b The premium and monthly storage payment information were obtained from a food-grade corn processor.

10 794 Y. A. Yigezu et al. Fig. 1. Optimal strategies for managing insects and moulds on 16 March.

11 Integrated joint pest management strategies 795 Fig. 2. Optimal strategies for managing insects and moulds on 16 June.

12 796 Y. A. Yigezu et al. Fig. 3. Optimal strategies for managing insects and moulds on 1 July.

13 Integrated joint pest management strategies 797 IDK is between 0 and per cent, the optimal decision on 16 March for all levels of the in-bin temperature is ACKP[18,20], indicating the optimal strategy is conditional aeration until sale between periods 18 and 20 (inclusive). The designation Fum 1 indicates that fumigation is unlikely to be needed given these starting conditions and the optimal strategies. Focusing on the same panel of Figure 1, when IDK is between and per cent the optimal insect management strategy is ACKP[18] regardless of in-bin temperature, indicating the optimal strategy is to aerate conditionally and sell to the processor in period 18. Fum ¼ 1 indicates the producer will not need to fumigate. The optimal management decisions vary substantially depending on the biophysical conditions inside the bin, ranging from keeping the grain for future sale without applying any control, to conditionally or unconditionally aerating and keeping the grain for future sale, to selling immediately. Several thresholds are qualitatively common across these figures. The first is represented by the vertical line at the extreme right (high IDK levels), at the level of IDK at which the grain is rejected by both the elevator and the food processor due to excessive total damaged kernels 20 per cent. Thus, these lines appear at 20 per cent in all of the A panels, at 14.5 per cent for all of the C panels, and at varying levels for the B panels, reflecting the different average levels of MDK associated with the B panels. For the B panels, the rejection thresholds are 17.7 for 16 March, 16.0 for 16 June and 15.5 for 1 July. The second common threshold, in all figures and panels, is a strategy shift at the IDK level of per cent, which corresponds to the threshold of two live insects per kilogram, where the processor rejects the grain due to excessive live insects. Below this level, the likelihood of needing to fumigate is low, while above this level fumigation is needed to sell to the processor (this choice is always optimal if the level of total damaged kernels is in the acceptable range for the processor). Producers can avoid fumigation below the two live insects threshold with high probability by adjusting the date of sale. The third common threshold separates the states where sales are to the processor from states where sales are to the elevator. The boundary between these regions corresponds to starting conditions that lead to the level of total damaged kernels equal to 6 per cent. Because the level of MDK is the same for panels A and C in Figures 1 3, these vertical lines are in the same place (5.8 and 0.4 per cent for the A and C panels, respectively). For the B panels of Figures 1 3, these boundaries are at 3.7, 2.0 and 1.5 per cent, respectively. These three thresholds divide each of the nine temperature/idk plots in Figures 1 3 into four regions. We begin from the left-most region where IDK is below per cent, and the live insect population is low enough that fumigation will usually not be necessary. In the 16 March period, the optimal management strategy is to conditionally aerate and adjust the date of sale to avoid the need to fumigate with one exception. The exception is when temperature is extremely low (0 58C), IDK is low and MDK is very high, in which case the grain is not aerated and is sold on 16 April. This set

14 798 Y. A. Yigezu et al. of circumstances indicates a high probability of rapidly growing mould problems occurring in the near future, but also a current in-bin temperature that is low enough that conditional aeration is not needed. As we move from left to right in the region where IDK is below per cent, expected sales timing shifts to earlier periods. This pattern is repeated for the IDK below per cent region during the 16 June period (Figure 2), and the growth-suppressing effect of temperature on both mould and insects at low temperatures becomes apparent. The exception in this period is when MDK is high, and the optimal strategy is to sell immediately to the processor. This pattern repeats in the 1 July period (Figure 3), with the boundaries for expected sale dates shifting to the left (lower IDK) and down (lower temperature) and expansion of the region in which immediate sale is optimal. Now consider the region where IDK is above per cent, but total damaged kernels (IDK plus MDK) is less than 6 per cent. Here, the producer fumigates to guarantee that the grain will not be rejected for excessive live insects. As we move from left to right across this region, the optimal sale time shifts to earlier periods, and at higher levels of MDK, immediate sale to the processor is optimal. At higher levels of MDK (the C panels) and in all panels in the latter part of the year (Figures 2 and 3), the IDK boundaries are no longer parallel to the temperature axis. For example, in panel C of Figure 1, the panel labelled SP Fum ¼ 2 indicating immediate fumigation and sale to the processor is bounded below by an IDK level of 0.28 per cent for temperatures in the range from 0 to 58C. The lower bound switches to 0.12 from 5 to 258C and from 308C upwards. Between 25 and 308C, the IDK boundary drops to per cent. The reason for this non-monotonicity is that as temperature rises from low levels, conditions become more favourable for the growth of both mould and insects, and the optimal strategy is to sell earlier at a lower level of IDK. However, as the temperatures rise to higher levels, conditions become too warm for insects, and the likelihood of additional IDK decreases. This phenomenon occurs early in the year (Figure 1) only at high levels of MDK. Later in the year (Figures 2 and 3), this phenomenon occurs even at low levels of MDK and almost disappears for the highest levels of MDK. In the region where total damaged kernels (IDK plus MDK) is above 6 per cent but below the level at which the grain is rejected by both the processor and elevator for excessive damaged kernels, the optimal strategy is to aerate conditionally until March, when elevator prices peak, at which time the producer should fumigate and sell to the elevator. For periods beyond 16 March when IDK plus MDK exceeds 6 per cent, the optimal strategy is to fumigate and sell immediately to the elevator. If total damaged kernels exceed the rejection threshold for both the processor and the elevator, the producer has no recourse and cannot sell the grain. The model results indicate that conditional aeration is a highly effective strategy for managing mould and insects. Aerating only when the ambient temperature is at least 38C below the in-bin temperature is usually a superior strategy to unconditional aeration. The exception to this rule is found late in

15 Integrated joint pest management strategies 799 the storage season (Figure 3) when ambient temperature is typically high, the in-bin temperature is quite high (above 358C), MDK is low (0 per cent in panel A) and IDK is between and 0.15 per cent. In this case, unconditional aeration is optimal, in order to further heat the grain above the temperature conducive to insect growth Benefits of IJPM for controlling moulds and insects Table 3 displays the expected return above storage cost for the typical initial biophysical conditions (1 per cent MDK, 0.2 live insects per kilogram and a temperature of 388C) for three different marketing channels and four different management strategies. The marketing channels are (i) without contract (producers deliver to the elevator); (ii) a rigid contract (producers must deliver to the processor on 31 July or to the elevator at any time); and (iii) a flexible contract (producers can deliver to either the processor or the elevator and at the time they choose). The management strategies are (i) traditional management (aerate grain in the fall to lower the grain temperature to 58C and sell to the elevator in mid-march if there is no contract, or sell to the optimal buyer and with the optimal timing if there is a contract); (ii) IPM for moulds only (producer monitors for moulds but not for insects); (iii) IPM for insects only (producer monitors for insects but not for moulds); and (iv) IJPM (producer monitors for moulds and insects). For strategies (ii) and (iii), the unmonitored pests grow and damage the grain, but controls are applied only for the monitored pest. Without a food processor contract, the traditional strategy yields expected revenue net of storage costs almost as high as IPM for insects and IJPM. 7 This confirms the near optimality of producers traditional management strategy in the absence of a contract. The IPM mould strategy does much worse, underscoring the importance of monitoring for insect damage. Table 4 presents the probability that the producer delivers grain of acceptable quality to the processor on 31 July, which reflects the joint event that it is optimal for the producer to store until 31 July and that the grain quality is acceptable to the processor. This probability is a measure of contract s effectiveness with respect to the processor s goal to have enough quality grain to meet their needs. Assuming that the processor needs two months of grain delivered on 31 July to cover their needs until the following harvest, at least 16.7 per cent of the grain must meet the minimum quality standards and be stored until 31 July. Food processor contracts specify rigid delivery dates and require long-term storage to ensure a year-round supply of grain. Model results presented in Table 3 show that the IJPM strategy is optimal if the producer has a rigid contract, with expected net returns for IPM for insects only, IPM for moulds only, 7 The small difference in net return between the rigid contract and traditional practice with delivery to the elevator suggests that the contract is efficient. However, an evaluation of the contract s efficiency is beyond the scope of this article.

16 800 Y. A. Yigezu et al. Table 3. Expected revenue net of storage cost by management strategy (USD) a Expected revenue net of storage cost Management strategy Without contract Rigid contract Flexible contract Traditional 81,875 68, ,218 b IPM for moulds only 34,022 7,142 7,979 IPM for insects only 82,354 83,935 83,935 IJPM for moulds and insects 82,354 98, ,487 a The typical starting in-bin biophysical conditions on 16 October are 1 per cent MDK, 0.2 live insects per kilogram and a temperature of 388C. b The case of traditional management with a flexible contract corresponds to traditional, unconditional aeration, monitoring both pests and sale to either the processor or elevator at the optimal time. Table 4. Probability of delivering acceptable quality grain to food processor on 31 July a Rigid contract Traditional b IPM for moulds only IPM for insects only IJPM for moulds and insects Flexible contract a The typical starting in-bin biophysical conditions on 16 October are 1 per cent MDK, 0.2 live insects per kilogram and a temperature of 388C. b The case of traditional management with a flexible contract corresponds to traditional, unconditional aeration, monitoring both pests and sale to either the processor or elevator at the optimal time. and traditional management achieving 85, 7 and 83 per cent of the IJPM returns. A producer using IJPM and delivering to a processor under a rigid contract achieves returns that are nearly 21 per cent higher than delivering to an elevator without a contract. However, if the producer uses traditional management strategies (i.e. does not monitor for pests and apply controls as per the IJPM strategy), then he would be better off delivering to the grain elevator without a contract. From the food processor perspective, the rigid contract offers producers the correct incentives to use IJPM and results in 83 per cent of the grain being of acceptable quality on 31 July. Flexibility in timing-of-sales allows producers who use IJPM returns that are nearly 5 per cent higher than the rigid contract (Table 3), resulting from the ability to deliver to the processor before 31 July, which occurs 19 per cent of the time (Table 4). More importantly, if the producer has a flexible contract, traditional aeration with monitoring is almost as profitable as IJPM. For the processor, offering a flexible contract when producers use traditional practices would fail to meet their needs for a year-round supply since producers would fail to deliver any acceptable grain on 31 July.

17 Integrated joint pest management strategies 801 Fig. 4. Periodic mould damage and insect growth rates with no aeration: 16 June 30 June Pest responses to biophysical conditions Another reason for developing an IJPM programme for moulds and insects is that these pests respond to the same biophysical conditions, but with important differences in the growth response functions. Figure 4 illustrates the responses to temperature for a two-week period in June a critical time for pest development. Mean insect growth is estimated as a linear function of temperature without aeration and near zero with conditional aeration. In contrast, mean MDK growth with or without aeration is essentially zero until about 198C. Above 198C, MDK growth with conditional aeration remains essentially zero, but without aeration, mould growth increases exponentially with temperature. These are similar in the sense that conditional aeration can arrest growth of both pests. However, without aeration, there is a response to insect growth at all temperature levels, which is extremely rapid under warm conditions and negative under cold conditions. 8 In contrast, the growth of MDK without aeration is essentially zero at low levels of temperature and increases at an increasing rate at high temperatures. In addition, the temperature thresholds where pest growth becomes positive are different for insects (above 13 degrees) and moulds (above 18 degrees). 7. Conclusions While IPM has proved to be an effective approach to the economical control of pests, circumstances may arise in which the coordination of IPM for multiple pests is advantageous. We dub this approach integrated joint pest management (IJPM). The circumstances that suggest the use of IJPM include: some of the same growing conditions affect more than one pest, but with 8 Temperatures below 138C are rare during this period. The data used to estimate the relationship displayed in Figure 4 are quite sparse below 138C, and have extremely low probability in the dynamic programming model.

18 802 Y. A. Yigezu et al. different response functions or thresholds; controls for one pest have an impact on others; there are complementarities in monitoring costs; the pests are of similar economic importance. These circumstances are all in force for the case study presented here. The growth of moulds and insects is affected by temperature, but the response functions and thresholds are different; aeration and early sales can limit economic damage from both pests; both pests affect grain quality in an adverse manner that may have substantial revenue implications, especially for the producer with a food processor contract. Yigezu et al. (2008, 2010) show that IPM can be cost-effective for individually managing these pests, with aeration of the stored grain and timing of sales being highly effective for economically controlling the individual pests. This article applies IJPM to the case of managing insects and moulds in stored corn in Indiana. Because some of the controls (e.g. aeration and timely marketing) suppress both pests, there are spillovers from controls that impact the optimal thresholds for control application. Additionally, both pests contribute to total damaged kernels, which may result in the loss of all value if it is extreme. Further, this article shows that it is profitable to develop an IJPM strategy, especially for producers that contract with food processors. In the absence of contracting, the contribution of IJPM is small, confirming that producers traditional practices are suitable. However, with a rigid contract, the IJPM gains relative to the traditional practice are quite large. With a flexible contract, IJPM and the traditional practice have similar returns because we assume that the producers using the traditional practice monitor the grain to decide when to sell; however, with traditional practice augmented by monitoring, deliveries occur, on average, in March and April time periods, failing to meet the food processor s goal of a year-round grain supply. Thus, food processors are unlikely to offer flexible contracts to producers who do not implement an IJPM programme. Finally, the gains from IJPM relative to IPM (i.e. monitoring for only one pest and ignoring the damage from other pests) are substantial for either contract type. This article contributes to the general IPM literature by developing and describing the optimal management policy in a case with multiple pests and multiple interventions. Zacharias and Grube (1986) and Monjardino, Pannell and Powles (2004, 2005) also describe the optimal policy for multiple pests in field crops but do not compare it with a single-pest IPM benchmark. Thus, this article is the first to demonstrate the benefits of IJPM relative to single-pest IPM and the first to describe the optimal policy for multiple pests for stored products. The development of an IJPM strategy is typically more computationally challenging because the state space and the set of controls are expanded. However, the case study presented here demonstrates that the benefits may be substantial. The derived strategy may involve more intensive use of controls that exhibit spillover effects because benefit accrues to the control of multiple pests. Thus, the optimal control strategy may be different than control strategies that are developed for individual pests and informally

19 Integrated joint pest management strategies 803 combined. Future work will incorporate forthcoming technology advances that will make monitoring-based strategies more cost-effective and easier to implement (Fleurant-Lessard, 2011; Nansen and Meikle, 2011). Acknowledgements The article is part of a large-scale, long-term effort among Purdue University, Kansas State University, Oklahoma State University and the USDA-ARS Grain Marketing and Production Research Center funded by the USDA-CSREES Risk Assessment and Mitigation Program (RAMP), Project No. S05035, entitled Consortium for Integrated Management of Stored Product Insect Pests, Collaboration of grain producers, handlers, processors and numerous equipment and service suppliers in this project is appreciated. We thank Phil Cheeseman and the Rosen Center for Advanced Computing for computational support in generating the data required for constructing the transition probability matrices from PHAST-FEM using the Condor System. References Adam, B. D., Mah, P. M., Phillips, T. W., Flinn, P. W. and Anderson, K. B. (2004). Is there any reason for businesses not to adopt IPM? The economics of IPM in stored grain. Presented at the Agricultural and Applied Economics Association Annual Meeting, August 1 4, 2004, Denver, CO. Accessed 21 December Adam, B. D., Siaplay, M., Flinn, P. W., Brorsen, B. W. and Phillips, T. W. (2010). Factors influencing economic profitability of sampling-based integrated pest management in stored grain. Journal of Stored Products Research 46: /j.jspr Accessed 8 November Anderson, K., Schurle, B., Reed, C. and Pedersen, J. (1990). An economic analysis of producers decisions regarding insect control in stored grain. North Central Journal of Agricultural Economics 12: Bellman, R. E. (1957). Dynamic Programming. Princeton, NJ: Princeton University Press. Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming. Princeton, NJ: Princeton University Press. Brooke, A., Kendrick, D., Meeraus, A. and Raman, R. (2005). GAMS. A User s Guide. Washington, DC: GAMS Development Corporation. Cowan, R. and Gunby, P. (1996). Sprayed to death: path dependence, lock-in and pest control strategies. The Economic Journal 106: Cuyno, L. C. M., Norton, G. W. and Rola, A. (2001). Economic analysis of environmental benefits of integrated pest management: a Philippines case study. Agricultural Economics 25: Fleurant-Lessard, F. (2011). Monitoring insect pest populations in grain storage: the European context. Stewart Postharvest Review 3: 4. Florax, R. J. G. M., Travisi, C. M. and Nijkamp, P. (2005). A meta-analysis of the willingness to pay for reductions in pesticide risk exposure. European Review of Agricultural Economics 32:

20 804 Y. A. Yigezu et al. Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D. and Gunsolus, J. L. (1996). Multiyear validation of a decision aid for integrated weed management in row crops. Weed Science 44: Fox, J. A. and Hennessy, D. A. (1999). Cost-effective hazard control in food handling. American Journal of Agricultural Economics 81: Greene, C. R., Kramer, R. A., Norton, G. W., Rajotte, E. G. and McPherson, R. M. (1985). An economic analysis of soybean integrated pest management. American Journal of Agricultural Economics 67: Hall, D. C. and Norgaard, R. B. (1973). On the timing and application of pesticides. American Journal of Agricultural Economics 55: Hyde, J., Martin, M. A., Preckel, P. V. and Edwards, C. R. (1999). The economics of Bt corn: valuing protection from the European corn borer. Review of Agricultural Economics 21: Magnusson, C. and Cranfield, J. A. L. (2005). Consumer demand for pesticide free food products in Canada: a probit analysis. Canadian Journal of Agricultural Economics 53: Maumbe, B. M. and Swinton, S. M. (2003). Hidden health costs of pesticide use in Zimbabwe s smallholder cotton growers. Social Sciences and Medicine 57: McNamara, K. T., Wetzstein, M. E. and Douce, G. K. (1991). Factors affecting peanut producer adoption of integrated pest management. Review of Agricultural Economics 13: Monjardino, M., Pannell, D. J. and Powles, S. B. (2004). The economic value of haying and green manuring in the integrated management of annual ryegrass and wild radish in a western Australian farming system. Australian Journal of Experimental Agriculture 44: Monjardino, M., Pannell, D. J. and Powles, S. B. (2005). The economic value of glyphosate-resistant canola in the management of two widespread crop weeds in a western Australia farming system. Agricultural Systems 84: Montross, M. D., Maier, D. E. and Haghighi, K. (2002). Development of a finite-element stored grain ecosystem model. Transactions of the ASAE 45: Mullen, J. D., Alston, J. M., Sumner, D. A., Kreith, M. T. and Kuminoff, N. V. (2005). The payoff to public investments in pest-management R&D: general issues and a case study emphasizing integrated pest management in California. Review of Agricultural Economics 27: Mullen, J. D., Norton, G. W. and Reaves, D. W. (1997). Economic analysis of environmental benefits of integrated pest management. Journal of Agriculture and Applied Economics 29: Nansen, C. and Meikle, W. G. (2011). The economic injury level and action threshold in stored-product systems. Stewart Postharvest Review 3: 3. Phillips, T. W., Noyes, R. T. and Adam, B. D. (2002). Integrated pest management for grain elevators that supply the breakfast cereal industry: case studies and economic analysis. Final report to the Grocery Manufacturers of America and the National Foundation for IPM Education. Unpublished manuscript. edu/publications/osu-elevator-ipm.pdf. Accessed 20 December Rejesus, R. M., Palis, F. G., Lapitan, A. V., Chi, T. T. N. and Hossain, M. (2009). The impact of integrated pest management information dissemination methods on