Final Report for GW09-012
The wheat stem sawfly, Fusarium crown rot, and grassy weeds form an interacting multi-trophic pest complex affecting farmers across the Northern Great Plains. Our goal was to develop a framework for modeling and analyzing data within the context of a total-system approach to pest management. We conducted field studies to evaluate the relative importance of crop variety, seeding rate and weed management approach on insect pests, plant pathogens and grassy weeds. While our results indicated complex interactions in the response of these pests to management, our modeling allowed us to pinpoint first principles of a total-systems approach to pest management.
The Northern Great Plains is a leading producer of small grain, pulse and oilseed crops and has strong potential for low-intensity organic agriculture (USDA ERS, 2011). However, the high commodity specialization (i.e. wheat) within conventional agriculture has resulted in vast monocultures and an influx of a specialized pest complex that compromises the robustness, resilience and environmental integration required in sustainable farming systems (Gan et al., 2010; NRC, 2010). In this region, wheat stem sawfly (Cephus cinctus, WSSF hereafter), Fusarium crown rot (Fusarium hereafter) and grassy weeds, represented in this project by Bromus tectorum (downy brome or cheatgrass, cheatgrass hereafter), form an interacting multitrophic pest complex which is a major burden to wheat cropping systems. Lack of viable pesticides for WSSF and Fusarium and the existence of herbicide resistant weed biotypes challenge the chemically-based, single-pest control paradigm. Moreover, each one of these pest groups responds differently to management practices, and contradictions among current single-pest recommendations may, in turn, lead to undesirable consequences (Shennan, 2008).
For dryland agriculture to meet increasing demands for yields, while sustaining the environment’s economic, social and ecological services, it is necessary to achieve a systems-level understanding of the linkages among biophysical processes, human activities and socioeconomic goals (Robertson et al., 2008). To facilitate such understanding, our main goal was to develop a framework for modeling and analyzing data within the context of a total-system approach to pest management (Lewis et al., 1997). To achieve our goal, we formed a multidisciplinary team composed of entomologists, plant pathologists, weed scientists and agronomists. To encompass a range of environmental conditions, we conducted our research in different locations across the Northern Great Plains.
Objective 1. Quantify interactions among wheat stem sawfly, Fusarium and grassy weeds in their joint impact on crop yield and in response to simple control practices, such as seeding density, herbicide management approach and crop variety.
Objective 2. Develop a predictive computer model to assess the relative importance of seeding density, herbicide management approach and crop variety on crop yield and the population dynamics of wheat stem sawfly, Fusarium and grassy weeds.
Objective 3. Develop and deliver an education and extension program to increase producers’ knowledge base on the ecological basis of multi-pest management strategies.
The research included three complementary field studies conducted in four states across the Northern Great Plains (Fig. 1). First, we performed a comprehensive evaluation of the relative importance of crop variety, seeding rate and weed management approach on insect pests, plant pathogens and grassy weeds. This detailed study took place between 2008 and 2010 at three locations across Montana (Amsterdam, Ulm, and Havre) and allowed us to pinpoint first principles related to multitrophic pest interactions.
Second, the degree to which the information generated in this exhaustive study applies to different regions across the NGP was evaluated in 2009 and 2010 by conducting a reduced version of the same experimental design in Ririe, Idaho; Lingle, Wyoming; and Hettinger, North Dakota.
Finally, at a fourth field site in Montana (Bozeman), we attempted to directly manipulate pest levels with the goal of assessing interactions among three pest groups and their synergistic impacts on wheat. Unfortunately, we failed to establish this study due to low cheatgrass emergence in 2009 and devastating hail storms at crucial times during the experiment in 2009 and 2010. Nevertheless, we conducted an additional study in 2010 to assess the degree to which WSSF cut stems were infested with Fusarium.
At each site in Montana, the experiment followed a split-split plot design with three replications. We planted three wheat cultivars to main plots: solid-stem -- i.e. WSSF tolerant, drought tolerant -- i.e. cheatgrass and Fusarium tolerant, and a high yielding variety. We used Choteau, McNeal and Reeder as representatives of these three cultivar types, respectively. At each site, all three cultivars were planted within sub-plots at half, recommended or double the seeding rates (100, 200, and 400 plants m-2, respectively). To generate a range of cheatgrass densities, sulfosulfuron was applied at three rates (0.8 of labeled rate, 0.4 of labeled rate and 0.2 of labeled rate no herbicide depending on the specific weed infestation conditions) plus a non-ionic surfactant. Herbicides were applied to achieve a range of cheatgrass abundance to randomly assigned sub-sub-plots. All other broadleaf and grassy weeds were controlled with standard chemical treatments, including pyrasulfotole -11oz/A-, florasulam plus fluroxypyr -1/3 pint/A- , pinoxaden -8.2 oz/A-, glyphosate -6 oz/A-, bromixynil plus MCPA -12.6 oz/A, and glyphosate plus 2,4-D in alleyways and field edges. At three sites in North Dakota, Idaho and Wyoming we planted the WSSF tolerant and drought tolerant cultivars at two seeding rates and three herbicide application rates. All practices were conducted following conservation tillage practices.
At the sub-sub-plot level, we obtained visual estimates of cheatgrass abundance, wheat yield (grain weight and test weight) and protein content. In Montana, WSSF abundance, Fusarium infection rates, as well as crop and weed emergence, abundance, biomass and seed production were estimated, utilizing three 0.485 m diameter rings within each sub-sub-plot, for a total of 243 samples per site/year. Over 100 samples of pest levels and yield measures were obtained at each of our satellite sites in Idaho, Wyoming and North Dakota for a total of 350 samples.
Fusarium levels were determined by visually assessing discoloration of every standing (not lodged by WSSF) wheat stem sampled in the two rows contained within each one of the three rings established per sub-sub-plot. Disease status was ranked as 1) none/low, 2) medium or 3) high. Two measures were developed for every sample: 1) Fusarium infection, which is the number of stems in categories medium and high divided by the total number of stems in the ring, and 2) Fusarium intensity represented by the number of stems in the high category divided by the number of stems in categories medium and high.
Visual assessments of Fusarium levels within wheat stems are usually limited to uncut stems due to discoloration sawfly feeding causes within cut stems. To deal with the absent data, it is assumed that cut stems were absent of Fusarium populations because Fusarium readily attacks and kills wheat stem sawfly. To test whether this assumption was valid, 46 paired crown samples (from uncut and neighboring cut stems) were collected at the Ulm test site. From these individual samples, DNA was extracted and Fusarium populations were measured as quantity of “Fusarium DNA” using quantitative real-time PCR (Hogg et al., 2007). Results of this experiment were used to develop weights for each severity class to be used in calculating weighted mean disease severity scores.
From each ring within sub-sub-plots, samples were collected at harvest time. Whole plants were uprooted and stems individually dissected. Data collected included number of uninfested and infested stems and number of stems with sawfly-induced lodging (larval survival to overwintering stage). Additional information collected included number of parasitized larvae (attacked by endemic braconids) and number of cadavers with visible fungal hyphae.
Early in the growing season, we obtained sub-sub-plot visual estimates of cheatgrass abundance. Additionally, cheatgrass abundance was visually estimated in each one of the three rings and two additional rings. All cheatgrass and wheat plants growing inside these two additional rings were harvested, dried to constant weight and weighed. Cheatgrass in each of the three within sub-sub-plot rings was again assessed visually, harvested, dried to constant weight and weighted at harvest time.
We monitored wheat after emergence by counting individual plants at the T1 stage in three separate 33 cm transects randomly established within each sub-sub-plot. Preharvest wheat sampling for WSSF consisted of 25 sweep net samples randomly in each cultivar at the time of WSS flight and stem elongation. At the Montana sites, sawflies were collected in a random replicate for each cultivar. The purpose of these early samples was to assess adequate potential pressure. This condition was always met and no further data were collected.
To parameterize our model with a data set comprising of field observations of multiple pests, we had to overcome problems of coverage and confounding outcomes. These problems are inherent from the fact model predictors (pest levels) are linked by causality, e.g. the presence of one pest could cause the increase of another. Thus, the estimates of impact of treatment on any one pest, or pest on the crop are confounded by the presence of the other pest.
We adopted a Bayesian decision theory approach in combination with path analysis to evaluate interactions between pest groups in response to management practices (crop variety, seeding rate and herbicide application rate). Path analysis consists of several stages: first, constructing a causal network, a graphical representation of all the variables of interest with their causal links represented by directed arrows between them. Second, the graph is converted to a system of equations, such that every node with arrows pointing to it is a response predicted by the nodes where the arrows originated. In this setup a variable which is a response in one equation can be a predictor in another. Third, these equations are fit simultaneously to produce the path model. Last, path coefficients for the total effect of one variable on another can be calculated as the product of coefficient estimates along each path and summing all paths between the two.
Under the Bayesian model, paradigm prior knowledge about a parameter is updated with data to form a posterior probability distribution and the Bayes estimator minimizes some loss function (such as least square errors) on the posterior. This approach to analysis fits into the process of decision making in natural resource management, where scientific evidence is used to make more informed decisions and choose the scenario that minimizes risk (Ellison, 1996).
Bayesian modeling by Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method where the joint posterior is hierarchically partitioned according to Bayes rule into conditional distributions. Samples from the joint posterior can then be obtained iteratively by sequential sampling from the set of conditional distributions. This method is well suited for path analysis because the conditional distributions correspond to model equations in path analysis. We developed a procedure where we replace variables in the conditional distributions with residuals from the lower level conditionals. This procedure essentially creates internal (to the model) links between estimators of different pest impacts. Thus, our analysis method simplified conducting path analysis using Gibbs sampling because total effects and their variance were automatically obtained during model fitting process, and predictions better reflect the total systems approach to understanding the impact of multiple interacting pests on crop yield. All analysis was conducted in R 2.11.0 (R development core team, 2010) and JAGS 1.0.3 / 2.1.0 through rjags (Plummer. 2010).
For normally distributed data, using non-informative priors, Bayesian estimates reduce to the maximum likelihood estimates obtained in a frequentist analysis, because the posterior is a combination of the prior and the data’s sample distribution. Therefore whenever possible, we used frequentist tools such as generalized linear mixed effects models, and random deviates from these estimates and standard errors were used as posterior samples. The posterior can then be used to calculate probabilities for the outcomes of management scenarios, predict yields under different scenarios or construct highest posterior density intervals (interval where the posterior density is maximized) to compare and contrast estimates.
Results from our experiments indicate that yield differences can be readily and fully explained as a result of the effects of management on pests and multi-pest interactions, rather than just by the direct effect of any particular management scheme on yield. Different pest interactions under different management schemes and environments (sites, years) make a total systems approach to the analysis imperative and highlight the need to update current single-pest management recommendations.
The low disease incidence and low cheatgrass pressure observed during our study period at the Ulm precluded us to assess the joint response of pathogens and weeds to management practices utilizing data gathered at this site. Nevertheless, we were able to accurately analyze the joint response of Fusarium and cheatgrass to management practices with data obtained at the Amsterdam (2008) and Havre (2009) sites. Cheatgrass biomass and cover increased the odds of high Fusarium intensity for all management practices but had a less consistent effect across management practices on Fusarium infection (Fig. 2).
Because Fusarium primarily develops during periods of water stress, disease severity is expected to increase with increasing plant biomass, and cultural controls are often related to moisture management such as reduction in seeding density (Cook, 1980; Paulitz et al., 2002). Therefore, we expected that disease severity to be lower at reduced wheat seeding densities. Unfortunately, this strategy may not be suitable for wheat productions systems in the NGP, as reducing seeding rates is counter to weed control strategies. In contrast, high initial seeding rates may help suppress cheatgrass emergence and plant vigor, mitigating the effect of cheatgrass on Fusarium. Thus, to properly model the effects wheat cultivar and seeding rate on Fusarium, it is necessary to incorporate both the direct and indirect effects of the presence of cheatgrass on disease abundance and severity.
The joint evaluation of the impact of management practices (wheat variety and seeding rate) on disease and weed pressure indicated that the drought tolerant cultivar, also resistant to Fusarium, significantly suppressed cheatgrass at high seeding rates (Table 1), which explains the unexpected significant reduction in Fusarium intensity observed in that treatment. The significant increase in Fusarium in the WSSF tolerant cultivar could be explained by similar logic, except in this case the WSSF tolerant cultivar is a weak competitor and failed to suppress cheatgrass (Table 1). Although the magnitude of the effect of cheatgrass on Fusarium reported in Table 1 may seem low, it is the effect of every one gram increase in cheatgrass. Thus, for example, the odds ratio of high intensity Fusarium infection in the vicinity of a 100 gram plant of cheatgrass is approximately 6 (e100*.018), with a 95 % highest posterior density of 1.5 – 14.
The Fusarium – cheatgrass interaction results presented in Table 1 were obtained under extreme levels of WSSF infestation and refer to uncut (lodged) stems only due to our inability of visually determining Fusarium status for lodged stems. To assess the accuracy of these results and further investigate the joint impact of pathogens and insects on crop yield, we collected 46 paired of WSSF cut and standing samples of 20 stems each from a range of crop variety and seeding rate conditions. These samples were visually scored for Fusarium as well as thru quantitative PCR. Pair-wise comparisons of cut versus uncut samples showed no statistical differences in Fusarium populations (t = -1.10, p = 0.278). This result indicates that, first, Fusarium populations within cut stems are similar to those described by visual assessments of neighboring uncut stems. Second, the Fusarium – cheatgrass interactions detected in Table 1 are not confounded by the presence of WSSF. Finally, this experiment showed differential measurement errors in visual Fusarium assessment by treatment Agreement between the two methods to assess disease status (visual and qPCR) was high for WSSF tolerant cultivar, and low for the other two (Fig. 3). This result suggests that WSSF larval burrowing could suppresses the symptoms of Fusarium, but further tests are required).
Very heavy WSSF infestations were observed at all three Montana sites in all years. To assess WSSF levels, lodged stems in the sample rings (total 729 rings per year) were separated from the rest of the sample, counted and threshed for grain yield. Once Fusarium scoring was completed, individual stems were dissected to assess WSSF larva infections and threshed for grain yield. WSSF data were scored in four categories; 1) uni: a clean stem were no larva is found, 2) inf: a stem were a dead WSSF larva is found, 3) par: a stem where a parasitoid of WSSF was found, i.e. the stem was attacked by the WSSF but the larva or eggs were killed by a parasitoid, and 4) cut: the WSSF completed the life cycle and caused the stem to lodge before harvest. In this study, WSSF cutting was assumed to be equivalent to a 40% yield loss, as 10-20% loss is attributed to the larva burrowing through the stem during the growing season (Holmes 1977) or losses may be greater when plants are nutrient or water deficient (Delaney et al 2010) and approximately 25% results from failure to harvest due to stem lodging (Ainslie 1920, Beres et al 2007). Under low cheatgrass pressure, we found WSSF attack rates and cutting rates significantly lower on the solid stem WSSF tolerant cultivar (p < 0.001) (Table 2). However, these differences did not translate into significant yield increases at the plot level. This result may be attributed to our research size combine collecting more lodged stems than a commercial one could or the inherently higher yields of the drought tolerant and high yielding cultivars under dry conditions, which compensated for the increased cutting and burrowing.
The impact of WSSF on crop yield was modified by the presence of grassy weeds. Specifically, cheatgrass modified WSSF oviposition choices (Fig. 4) and cutting rate (Fig. 5). WSSF oviposit in cheatgrass present at low densities in wheat fields despite low success rates for completing its life cycle (Perez-Mendoza et al. 2006). Consequently, we observed a decrease in WSSF attack rates on wheat as well as an increase in attack rate variability as cheatgrass levels increase (Fig 4). This effect was dependent on cultivar, with the WSSF tolerant variety having the lowest mean attack rates under low cheatgrass pressure but exhibiting the most change in the shape of the posterior distribution with increasing cheatgrass levels. In contrast, the drought tolerant variety, which is the more competitive cultivar and thus less impacted by increasing cheatgrass levels, exhibited a decreasing mean attack rate and increasing variability.
The observed differences in cheatgrass impact on wheat yield across cultivars suggest that WSSF choice was not modified simply due to the increased frequency of an alternative host (cheatgrass). Since WSSF is known to oviposit based on stem diameter, a “proxy” for fitness based on the fact that haplodiploid hymenopteran species produce more females from wide stems and more males from narrow ones (Wall, 1952, Morrill et al. 2000, Carcamo et al. 2005) reference), it is likely that plant competition with cheatgrass resulted in a higher frequency of smaller and narrower wheat stems, which are subsequently less preferred by WSSF females (Buteler et al. 2009). Further evidence of the role of plant competition in mediating WSSF oviposition choices is the fact presence of cheatgrass competition reduces wheat fitness (Fig. 6), which resulted in increased lodging in WSSF tolerant and high yielding cultivars, especially at high seeding rates, but not for the more resilient drought tolerant cultivar (Fig. 5).
When jointly considering the three pest groups (insects, grassy weeds and pathogens), the presence of biological interactions occurring at different trophic levels may, in turn, lead to non-intuitive outcomes of management practices. Despite these difficulties, our decision models accounted for the presence of multiple direct and indirect interactions of management treatments on crop and pests.
Path analysis can be a useful tool in natural resource management where indirect effects can have significant impacts on outcomes. A path model consists of all direct and indirect effects in the system, but current methodology relies on calculating indirect effects by post hoc tracing all direct effects along every path. For complex networks with a large number of connected indirect paths, this process, as well as extracting accurate standard errors for path coefficients, becomes cumbersome. We developed a model fitting procedure which, when implemented in a Bayesian Decision Network context, directly derives total effects (direct and indirect) and their variance estimates for all variables in the path model. Our procedure also overcomes problems of convergence and bias which are common in complex network analysis (Fig. 7) and allows easy derivation of the estimate error and construction of confidence interval for path coefficients. Also, since all paths (direct and indirect) are derived in one step, it may help expand implementing total systems models to more complex networks and make more accurate predictions of the long term effects of treatments on crop in agronomic settings.
The Bayesian network approach to path analysis allowed us to provide probabilistic answers to outcomes of management decisions. It also allowed us to make predictions based on specific management goals, such as maximizing yield in the presence of multiple pests occurring at different trophic levels. For example, WSSF tolerant varieties should be planted at a low seeding rate under high WSSF pressure. However, this variety should be replaced by a more competitive and drought tolerant cultivar at high seeding rates as cheatgrass levels increase, despite the persisting WSSF infestation (Fig. 8).
- Figure 3. Frequency of stems in each Fusarium level category of the visual method, sorted by number of Fusarium DNA copies determined with qPCR (red curve). For the WSSF tolerant, except for an odd observation in position six, visual estimates generally follow the qPCR results while the other cultivars do not, especially at high levels.
- Figure 4. Fraction of WSSF infested wheat stems in Amsterdam, MT 2009. Samples were grouped by cultivar (rows) and quartiles of cheatgrass visual estimate of volume (m-3) (columns). The overlay beta distribution curve represents the posterior oviposition rate with a non-informative prior.
- Figure 6. Wheat yield in the sampling rings (not adjusted for losses due to WSSF burrowing) relative to cheatgrass abundance at two sites in 2009 for all cultivar by initial seeding rate combinations. A similar pattern was observed in 2008 (data not shown).
- Figure 7. Four MCMC chains in a simulated simple triangle network (X2 = ßX1+ex ; Y = ?1X1 +?2X2+ey) with normally iid errors. The fit was done through a “classical” path analysis procedure (bottom row) or with the new procedure (top row). All simulated direct effects have a true value of 1, thus the combined direct and indirect effect is equal 2 (top left panel). The superiority of fitting the new estimators a’s over the old ?’s is demonstrated by: 1) convergence is achieved faster, Gelman and Rubin diagnostic Rhat is close to 1 (less then .05) after 50 iterations, 2) Any bias introduced by collinearity in ?’s is less apparent in a, 3) The algorithm is better at “sharing the information” contained in X1 and X2 to get joint estimates, as apparent in the convergence path of chain number 1 (solid black line), and 4) an estimate of direct and indirect effects is immediately derived with correct variance in a1 with no need to calculate it from separate direct path coefficients (top left panel).
- Table 1. Response of cheatgrass and Fusarium infection and intensity to wheat management practices. Models parameterized using values obtained at the Havre, MT location in 2009.
- Figure 2. Effect of 1 g increase in cheatgrass on the odds ratio of Fusarium infection (left panel) and Fusarium intensity (right panel) at the Amsterdam field site in 2008. Distributions are 95% highest posterior densities for every high yield (HY), drought tolerant (DT) and WSSF tolerant (SFT) cultivar by seeding density (low – l, medium – m and high – h) combination. Letters on the right side of left panel denote significant (>95%) overlap between treatment combinations. All posteriors overlapped in the right panel. Posteriors were generated independently for each treatment combination.
- Figure 5. Lodging (WSSF cutting) rate modified by cheatgrass plotted for cultivar and initial seeding rate. Size of the point character represents number of stems available, and thus also precision of the estimate.
- Figure 8. Posterior predicted yields of WSSF tolerant and drought tolerant at low and medium seeding rates. Models were parameterized using pest and crop values obtained in Amsterdam, MT in 2008. WSSF levels were high and assumed a 40% loss of yield from cut stems. Fusarium levels were low and assumed zero in this model. Percent cheatgrass referrers to a fraction of the maximum cheatgrass biomass observed in this field.
- Odds ratio* for rates of WSSF oviposition rate and lodging by cultivar for cheatgrass free sub-sub-plots at all three Montana sites over two years. Values reported are of the mode and 95% highest posterior density interval of 10,000 draws.
Educational & Outreach Activities
We presented our results at several extension/outreach meetings including the 2008, 2009 and 2010 Montana State University Crop and Pest Management School; the 2008, 2009 and 2010 Crop and Weed Annual Field Day; and the 2008, 2009 and 2010 Pest Management Tour. Several news releases and technical bulletins were developed and delivered based on information generated in this project. We also presented results of this study at the several professional meetings, including Ecological Society of America, the Weed Science Society of America and the WERA-97 Diseases of Cereals meeting.
Gan, Y., H.R. Kutcher, F. Menalled , G. Lafond , and S.A. Brandt . 2010. Intensifying production of using broadleaf crops in cereal-based cropping systems in the Northern Great Plains. Current Advancements in Soil Science and Agronomy Research in the Northern Great Plains. D. Malhi, ed., pp 277-299. Research Signpost, Kerala, India.
Menalled, F. 2010. Ecological considerations in the design of integrated weed management programs (In Spanish). Agroecologia 5: 73-78.
Sciegienka, J, E. Keren, and F. Menalled. In Print. Impact of root fragment dimension, weight, burial depth, and water regime on Cirsium arvense emergence and growth. Canadian Journal of Plant Science.
Smith. R., M.R. Ryan, and F. Menalled. 2011. Direct and indirect impacts of weed management practices on soil quality. Soil Management: Building a Stable Base for Agriculture. J. Hatfield and T. Sauer, eds., pp 275-286. American Society of Agronomy Series.
Keren, I, J. Robinson-Cox, and F. Menalled. A methodology to combine direct and indirect effects coefficients in causal networks. In preparation for Annals of Applied Statistics
Keren, I, J. Robinson-Cox, F. Menalled, D. Weaver, and A. Dyer. Accounting for indirect effects improves inference on treatments in agroecological experiments and observational studies. In preparation for Ecological Applications
Keren, I., D. Weaver, and A. Dyer, and F. Menalled Wheat stem sawfly larva burrowing obscures symptoms of Fusarium crown rot in spring wheat. In preparation for J of Plant Pathology
2010. Montana State University. Dept. of Plant Sciences and Plant Pathology. ”Assessing complex interactions in ‘simple’ systems. Bringing an ecological perspective to the study of wheat-fallow fields.”
2009. Montana State University. Department of Land Resources and Environmental Sciences. “Integrated Management of Agricultural Weeds in Montana.”
2008. South Dakota State University. Dept. of Plant Sciences. “Bringing a population and community ecology perspective into weed science.”
2008. Montana State University. Dept. of Plant Sciences and Plant Pathology. “Future challenges and opportunities in weed science.”
Keren, I, F. Menalled, D. Weaver, A. Dyer, and J. Robinson-Cox. 2011. A total system approach to modeling and pest management in wheat cropping systems. Montana State University Graduate Student Colloquium. March 2011. Bozeman, Montana.
Keren, I., F. Menalled, D. Weaver, A. Dyer, and J. Robinson-Cox. 2010. A total system approach to modeling and pest management in wheat cropping systems. 2010 Ecological Society of America Annual Meeting. Pittsburg, Pennsylvania. Aug 1-6, 2010.
Keren, I., F. Menalled, D. Weaver, and A. Dyer. 2010. Cheatgrass (Bromus tectorum) management in spring wheat cropping systems is complicated by the presence of wheat stem sawfly (Cephus cinctus) and Fusarium crown rot. 2010 Weed Science Society of America and Society for Range management Joint Annual Meeting. Denver, Colorado. Feb 7-11, 2010.
Alvarez, J.M. and F. Menalled. 2010. A new host and overwintering site for the mealybug Trionymus haancheni McKenzie (Homoptera: Pseudococcidae) in Idaho. 94th annual PB-ESA meeting. Boise, ID, April 11-14, 2010.
Keren, I. F. Menalled, D. Weaver, A. Dyer, and J. Robison-Cox. 2009. A proposed analysis for the total system approach to pest management in agriculture. Annual Meeting of the Montana Chapter of the American Statistical Association. Butte, MT. September 15, 2009.
Keren, I. F. Menalled, D. Weaver, A. Dyer, and J. Robison-Cox. 2009. Joint management of wheat stem sawfly (Cephus cinctus), Fusarium crown rot, and cheatgrass (Bromus tectorum): assessing the ecological basis of a total systems approach to pest management strategies. 94th Ecological Society of America Meeting. Albuquerque, New Mexico. August 2-7, 2009.
Burrows, M., D. Weaver, and F. Menalled. 2010. 2011 Common Montana Wheat Pests calendar. Montana Wheat and Barley Committee and Montana State University Extension
McVay, K., M. Burrows, F. Menalled, and K. Wanner. 2010. Montana wheat production guide. Montana State University Extension Service.
Burrows, M., D. Weaver, and F. Menalled. 2009. 2010 Common Montana Wheat Pests calendar. Montana Wheat and Barley Committee and Montana State University Extension.
McVay, K., M. Burrows, C. Jones, K. Wanner, and F. Menalled. 2009. Montana barley production guide. Montana State University Extension Service.
Burrows, M., Dyer, A., Grey, W. 2010. Small grain root and crown diseases. Montguide: Extension Distribution Center
To validate the responses observed in Montana, a reduced version of this experiment was conducted in Wyoming, Idaho and North Dakota. Pest levels and yields varied and were much lower at most sites than in our Montana site. Thus, despite efforts to standardize our protocol across sites, it was not always feasible to collect the same type of data collected in Montana. Thus each state-by-year combination was analyzed separately as a split-split plot design ANOVA, with Fusarium, cheatgrass, degree of WSSF infestation and lodging entered as linear annihilated covariates, i.e. treatment effects reflect the total effect of treatments “through” their effect on the pests of interest.
Yields varied between sites, being generally lowest in Wyoming and highest in Idaho, as well as between years (Figure 9). In general there were no significant effects of cultivar and seeding rate treatments at all sites and years. This is partly a result of low pest pressures which increased measurement errors, but may also reflect that the number of observations (36 in Wyoming and North Dakota and 72 in Idaho every year) were too low for a complete path analysis, which requires larger amounts of data (Petraitis et al., 1996). Moreover, in a split-split plot analysis the whole plot treatments have lower denominator degrees of freedom (two for cultivar and four for seeding rates) which contributed to large standard errors for these treatments but not for herbicide application.
Overall, reducing herbicide application rates had little negative effect on yields except for North Dakota 2009 and Wyoming in 2010. In Wyoming and Idaho in 2009 plots with reduced or no herbicide had significantly higher yields. This surprising result may be related to potential positive effects of the presence of low frequency cheatgrass patches such as reduced WSSF infestation or suppression of other weeds which compete with wheat.
Ainslie, C.N. 1920. The western grass-stem sawfly. USDA Technical Bulletin No. 841.
Beres B. L., Carcamo H. A., and Byers J. R. 2007. Effect of wheat stem sawfly damage on yield and quality of selected Canadian spring wheat. Journal of Economic Entomology 100: 79-87.
Buteler, M. Weaver D. K., and Peterson R. K. D. 2009. Environmental Entomology: 1707-1715
Carcamo, H.A., Beres B.L., Clarke F., Byers R.J., Mundel H.H., May, K., and DePauw R. 2005. Influence of plant host quality on fitness and sex ratio of the wheat stem sawfly (Hymenoptera : Cephidae). Environmental Entomology 34:1579-1592.
Cook, R. J. 1980. Fusarium foot rot of wheat and its control in the Pacific Northwest. Plant Disease 64:1061-1066.
Delaney K.J., Weaver D.K., and Peterson R. K. D. 2010. Photosynthesis and yield reductions from wheat stem sawfly (Hymenoptera: Cephidae): Interactions with wheat solidness, water stress, and phosphorus deficiency. Journal of Economic Entomology 103: 516-524.
Ellison, A.M. 1996. An introduction to Bayesian inference for ecological research and environmental decision-making. Ecological Applications 6:1036-1046.
Gan, Y., H.R. Kutcher, F. Menalled , G. Lafond , and S.A. Brandt . 2010. Intensifying production of using broadleaf crops in cereal-based cropping systems in the Northern Great Plains. Pages 277-299 in D. Malhi, editor. Current advancements in soil science and agronomy research in the Northern Great Plains. Research Signpost, Kerala, India.
Hogg, A.C., Johnston, R.H., Dyer, A.T. 2007. Applying real-time quantitative PCR to Fusarium crown rot of wheat. Plant Disease 91:1021-1028.
Holmes, N.D. 1977. Effect of wheat-stem sawfly, Cephus cinctus (Hymenoptera: Cephidae), on yield and quality of wheat. Canadian Entomologist 109: 1591-1598
Lewis, W.J., J.C. van Lenteren, S.C. Phatak and J.H. Tumlison III. 1997. A total system approach to pest management. Proceeding of the National Academy of Sciences 94: 12243-12248.
Morrill W.L., Gabor J.W., Weaver D.K. Kushnak, G.D. and Irish, N.J. 2000. Effect of host plant quality on the sex ratio and fitness of female wheat stem sawflies (Hymenoptera : Cephidae). Environmental Entomology 29: 195-199.
Paulitz, T.C., Smiley, R.W., and R.J. Cook. 2002. Insights into the prevalence and management of soilborne cereal pathogens under direct seeding in the Pacific Northwest, U.S.A. Can. J. Plant Pathology 24:416-428.
Perez-Mendoza, J. and Weaver D. K. 2006. Temperature and relative humidity effects on postdiapause larval development and adult emergence in three populations of wheat stem sawfly (Hymenoptera : Cephidae). Environmental Entomology: 1222-1231.
Petraitis, P. S., Dunham, A. E. and Niewiarowski, P. H. (1996). Inferring Multiple Causality: The Limitations of Path Analysis. Functional Ecology 10 pp. 421-431.
Plummer, M. 2010. rjags: Bayesian graphical models using MCMC. R package version 2.1.0-4. http://CRAN.R-project.org/package=rjags
R Development Core Team. 2010. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. http://www.R-project.org.NRC. 2010. Toward Sustainable Agricultural Systems in the 21st Century. National Academic Press, Washington, DC.
Robertson, G. P., V. G. Allen, G. Boody, E. R. Boose, N. G. Creamer, L. E. Drinkwater, J. R. Gosz, L. Lynch, J. L. Havlin, L. E. Jackson, S.T.A. Pickett, L. Pitelka, A. Randall, A. S. Reed, T. R. Seastedt, R. B. Waide, and D. H. Wall. 2008. Long-term agricultural research: A research education, and extension imperative. BioScience 58:640-643.
Shennan, C. 2008. Biotic interactions, ecological knowledge, and agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences 363:717-739.
USDA ERS. 2011. Organic production. http://www.ers.usda.gov/data/organic. Checked on July 12, 2011.
Wall, A. 1952. The diameter of the wheat stem in relation to the length and sex emerging sawfly (Cephus cinctus Nort). Scientific Agriculture 32: 272-277.
- Figure 9. Estimates and 95% confidence intervals for the effect of management practices on yield at 3 sites (ID – Idaho, ND – North Dakota and WY – Wyoming) over two years. The intercept (top left) represents yields for a WSSF tolerant cultivar at low seeding rate and herbicide applied at label rate. Panels represent the shift to the intercept value of changing management by increasing seeding rates (top), a drought tolerant cultivar (bottom left), increasing seeding rate of the drought tolerant cultivar (bottom) and reducing herbicide application rate (right).
No economic analysis was conducted as this was not a component of the proposed work.
This study did not contemplate a direct estimate of farmers’ adoption of a system-level of analysis to pest management. However, based on formal (pre- and post-presentation evaluations) and informal feedbacks obtained during our extension/outreach activities aimed at promoting holistic approaches to pest management, audience reception was highly positive.
Areas needing additional study
Understanding the complex multi-trophic interactions occurring between wheat stem sawfly, Fusarium and grassy weeds is a challenging task that surpasses the specific activities developed in this research project. While we were able to develop a framework for modeling and analyzing data within the context of a total-system approach to pest management, the observational nature of our work precluded us to identify mechanisms responsible for the observed results. Future research could contemplate manipulative study aimed at assessing interactions among three pest groups (wheat stem sawfly, Fusarium and grassy weeds) and their synergistic impacts on wheat. Also, it is necessary to evaluate the joint economic impact of these pests on wheat growers.