Duration: 10/01/2000 to 09/30/2005

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Weeds are the principle pests in Midwestern cropping systems, significantly reducing grain yield and quality. There is no doubt that herbicides are effective and useful tools for controlling weeds in these cropping systems. However, the costs associated with herbicide-based weed control are becoming increasingly greater and thus contribute to reduced profit margins. The cost of agricultural chemicals to U.S. producers was $7.6 billion in 1997, up from $6.1 billion in 1991. The prevalence and severity of weed infestations has also increased over the last century, despite the use of herbicides (Cousens and Mortimer, 1995; Ghersa and Roush, 1993; Wyse, 1994). This is because current crop/pest management systems lead to the presence of highly adapted weed species that exploit a given set of cultural, chemical, and environmental conditions (Wyse, 1994; Navas, 1991). Furthermore, the environmental and social impact of herbicide use is being extensively debated. These factors have lead to renewed interest in the integration of weed management methods that improve profitability, reduce environmental impact, and prevent the establishment of weed species that are highly adapted to a given management strategy (Cousens and Mortimer, 1995).

JUSTIFICATION:

Weed management decision-making is a complex endeavor requiring integration of weed biology, environmental risks, labor needs, crop yield potential, efficacy of a given control measure, and economics (Buhler et al., 1996). One way that growers and consultants can manage the integration of these complex factors is through the use of weed management decision support systems (DSS). Maxwell (1996) states that management-oriented DSS can improve our understanding of how weed biology and management strategies interact as well as to assess policy decisions and aid producers or consultants in making weed management decisions. Bioeconomic weed management models have been shown to produce adequate weed control recommendations resulting in a reduction in herbicide use/amount, a decrease environmental risk, and lower weed management costs (Forcella et al., 1996; Buhler et al., 1996; Buhler et al., 1997).

Successful implementation of a weed management DSS has the potential to greatly reduce the amount of herbicides applied in corn and soybean cropping systems, as well as to improve overall management of weeds and accurately quantify crop yield losses attributable to weeds. Since weed species, environmental conditions, and cropping practices vary across the Midwest, it is necessary to approach weed management DSS from a regional or bioregional perspective. Moreover, adoption of DSS will require a change in producer attitudes towards these tools.

Changing attitudes among producers will depend upon our ability to provide clear and consistent evidence that the benefits of using a DSS exceed the costs and that the risks of using these new tools are, at the very least, minimal.

Developing the database to quantify weed population dynamics is necessary before effective DSSs can be constructed. Obtaining these data will foster communication around a common conceptualization of the problem. Our conceptual models will be useful for organizing existing information, to help identify gaps in our knowledge and data needs, and for prioritizing research goals of individual cooperators. Existing and newly obtained data can be linked with these DSS and used to develop and refine further hypotheses and assess a broad range of management strategies (Maxwell et al., 1988). When incorporated into a bioeconomic DSS, the biological database developed in this project will provide the framework for objective evaluation of costs and benefits from weed control practices.

Results of research conducted by members of this project will expand the basic and applied weed biology knowledge base. This knowledge base will become increasingly important as farm practices shift toward more highly integrated management approaches. A number of NC-202 members have split research and extension appointments. These individuals continually help to insure that the results of this project are implemented by growers. Scientific benefits of the project arise through improved understanding of weed population processes and a free flow of ideas and data between cooperating researchers. Environmental benefits would result from the reduction of non-point source loading of herbicides and implementing strategies that reduce the overall use of herbicides. We estimate that this reduction could be as high as 29 million kilograms of herbicide active ingredient per year. Socioeconomic costs will also be reduced by significantly reducing the amount of money spent on external inputs. Additionally, a more information-intensive approach to weed management would facilitate greater communication among and between farmers, researchers, and environmentalists.

Regional variability in biological processes driving weed populations is poorly understood. Understanding the extent of and factors causing this variation will improve the reliability of crop loss estimates and reduce long-term costs of weed management practices. Moreover, focusing research needs in a cooperative and regional manner allows for the development of a database that is based on biological and ecological interactions among the crop, weed, and environment. The NC-202 project is a nationally recognized forum that focuses efforts of regional weed scientists on the relationship between weed ecology and management, with the goal of reducing our reliance on herbicide-based weed control strategies. NC-202 members have a very broad range of expertise in various aspects of weed management, including ecology, modeling, and agricultural economics. Moreover, complementary expertise of NC-202 members results in collective efforts that far exceed the contributions that members might make as individuals.

This proposal outlines a plan to enhance our understanding of factors driving weed/crop competition and their resulting impact on crop yield. This knowledge is essential if more information-intensive IPM approaches are to be implemented. The knowledge gained through this research will be used to parameterize and validate several existing weed management decision support systems. These DSSs (E.G. WeedSOFT, FoxPatch, and WEEDSIM) are effective tools for extending basic weed biology and management information to farmers.

Related, Current and Previous Work

Objectives

1. The following objectives are proposed to provide data for and refining a weed management DSS. Specific common protocol experiments will be conducted to address these objectives. Satellite projects conducted by individual project leaders will be conducted in support of these objectives. Project leaders will also draw upon the diverse interdisciplinary resources and expertise of the group to extend the inferences and knowledge gained in this research to related cropping systems and weed management problems
   
2. Understand the basis and relative importance of spatial, temporal, and biological variability in weed/crop competition.
   
3. Understand spatial, temporal, and biological variability of weed seed in the soil seedbank and it's impact on weed/crop competition
   
4. Develop a decision support module to evaluate economic risk for use in weed management DSS.
   
5. 
   

Methods

Objective 1. Understand the basis and relative importance of spatial, temporal, and biological variability in weed/crop competition.

This objective will comprise 4 main parts; a standard regional protocol, an expanded regional protocol, an economic analysis, and inclusion of individual research projects. The standard regional protocol will provide data that improves our understanding of basic weed biology and how this affects weed competitive ability. The expanded regional protocol will provide data to link weed biology information with crop yield loss. However, the expanded regional protocol will require significantly greater effort and resources to complete and will therefore be optional. The economic analysis segment of this objective will focus on risk assessment, providing data to support weed management DSS. We also left room in this objective to include individual research projects that have relevance to the overall goals of NC-202 but do not necessarily fit into the specific methodology of objective 1.

Standard Regional Protocol (Years 1-3):

Subobjectives:

1A) Determine the relative competitive indices of selected annual grass and broadleaf weed species in corn and soybean cropping systems.

1B) Determine the effect of time of emergence (i.e. cohort) on the relative competitive ability of these weed species.

Objective 1A will test the null hypothesis that the relative competitive indices of broadleaf weed species and grass weed species are equal within and between corn and soybean. Objective 1B will test the null hypothesis that the relative competitive ability is similar among cohorts within and among weed species in corn and soybean. A critical assumption of this objective is that above-ground weed volume and biomass will be predictive parameters for relative competitive indices of weeds and associated corn and soybean yield loss.

The experimental design will be a randomized complete block in a split-split plot arrangement with four or more replications. The main plot factor is crop species (corn and soybean), the subplot factor is cohort (four times of weed emergence), and the sub-subplot factor is weed species (four broadleaf species and four grass species). Subplot size will be 1.5 m (5 ft) by 9.1 m (30 ft). Glyphosate-resistant corn will be planted at 79,000 seeds ha^-1 in rows spaced 76 cm apart and glyphosate-resistant soybean will be planted 500,000 seeds ha^-1 in rows spaced 18 cm apart (i.e. drilled). The primary tillage system will be chisel plow. Target weed species will be selected based on the following criteria: that the weeds are common, competitive species in a region or state, that the species selected represent a range of typical emergence times (e.g. before or equal to crop emergence, equal to or later than crop emergence, or later than crop emergence), and that the weed species represent a range of growth habits. Example target weed species include the following:

Weed species	Emergence groupa
Annual Broadleaves
Common lambsquarter (Chenopodium album)	2
Giant ragweed (Ambrosia artemisiifolia)	2
Velvetleaf (Abutilon theophrasti)	3
Redroot pigweed (Amaranthus retroflexus)	4
Common waterhemp (Amaranthus rudis)	6
Annual Grasses
Woolly cupgrass (Eriochloa villosa)	3
Giant foxtail (Setaria faberi)	4
Barnyardgrass (Echinochloa crus-galli)	5
Yellow foxtail (Setaria glauca)	5
Large crabgrass (Digitaria sanguinalis)	7
aBuhler et al. 1997. Relative emergence sequence for weeds of corn and soybeans. Iowa State Univ. Ext. SA-11. 4 pp.

Approximately 10 seeds of a target species will be planted in a sub-subplot. Weed species will be spaced 1-m apart and 10 cm from the corn row or between soybean rows in each subplot. Weed seeds will be planted for each of four cohort treatments. Time of planting will be based on time of weed seedling emergence relative to crop growth stage:

	Cohort emergence time Crop growth stage
Cohort	Corn	Soybean
C1	VE	VE
C2	V3	VC
C3	V6	V3
C4	V10	V6

A wooden or plastic stake will mark the planting location of each target weed species. The soil at each planting location will be watered regularly to ensure weed seed germination and seedling emergence. Weed seedlings will be thinned by hand shortly after emergence to one per sub-subplot. After crop and weed emergence, non-target weed species will either be removed by hand or treated with glyphosate applied broadcast; target weed plants will be covered with plastic or styrofoam containers immediately before glyphosate application.

Weed height and diameter will be measured weekly throughout the growing season. Plant height will be the distance from the soil surface to the upper most portion of the free-standing plant. Average plant diameter will be determined from two perpendicular measurements at the widest portion of the plant. Cylindrical plant volume will be calculated from plant height and diameter measurements. Above-ground biomass and seed production of each weed species will be determined at or shortly after physiological maturity. Crop plant height, diameter, and growth stage will be recorded weekly. Competitive index of weed species will be determined by comparing dry weights, plant volumes, and relative growth rates to associated crop yield losses.

Standard Regional Protocol (Years 3-5)

Subobjectives:

1C) Incorporate weed competitive indices and time of emergence (i.e. cohort) effects into a weed management DSS.

1D) Determine corn and soybean grain yield loss associated with four cohorts of a multi-species weed community to use as validation data sets for the weed management DSS.

A separate experiment for corn and for soybean will be conducted at one or more locations. Plot size will be 3.0 m (10 ft) by 9.1 m (30 ft). The experimental design will be a randomized complete block with four or more replications. Glyphosate-resistant corn will be planted (79,000 seeds ha^-1) in rows spaced 76 cm apart and glyphosate-resistant soybean will be planted (500,000 seeds ha^-1) in rows spaced 18 cm apart (i.e. drilled). The primary tillage system will be chisel plow. Location of these experiments will be based on two or more target weed species included in experiments during years 1 through 3, but the relative abundance and densities of these species will be those of the in situ weed community. Five treatments will in be included:

1) non-treated	(cohort 1)
2) weed emergence at V2 corn or VC soybean	(cohort 2)
3) weed emergence at V4 corn or V1 soybean	(cohort 3)
4) weed emergence at V6 corn or V3 soybean	(cohort 4)
5) weed-free	.

Cohort treatments will be established by glyphosate application at specific stages of crop growth. Weeds that emerge prior to those in a specified cohort will be treated with glyphosate, or removed by hand if needed, in a timely manner such as to prevent any impact on crop growth and yield. The weed-free treatment will be maintained by the use of glyphosate and weed removal by hand as needed.

Density, height, and diameter of each weed species within each cohort will be determined weekly after initial time of emergence in two quadrats (0.25 m by 0.25 m) in each plot. Aboveground biomass and seed production of each weed species will be determined in each quadrat at or shortly after physiological maturity. Crop plant height, diameter, and growth stage will be recorded weekly. Crop grain yield will be quantified after collection of weed biomass and seed data.

Optional Expanded Protocol (Years 1-3)

The specific objectives for this protocol are similar to those described above for years 1 through 3, but the experiments are designed to determine relative competitive indices of weed species based on measured corn and soybean yield loss rather than on parameters that describe weed growth, development, and productivity. Consequently, plot size, time, and labor requirements for this protocol will be substantially greater than those for the standard regional protocol. The specific objectives of the optional expanded protocol for years 1 through 3 are to:

Subobjectives:

1E) Determine the relative competitive indices of selected annual grass and broadleaf weed species based on corn and soybean yield loss.

1F) Determine the effect of time of emergence (i.e. cohort) of these weed species on corn and soybean yield loss.

A separate experiment will be conducted for corn and for soybean. The experimental design will be a randomized complete block in a split-plot arrangement with three or more replications. The main plot factor will be cohort and the subplot factor will be weed species. Subplot size will be 3 m by 3 m. An alternative experimental design could include weed species density as a treatment factor.

Glyphosate-resistant corn will be planted (79,000 seeds ha^-1) in rows spaced 76 cm apart or glyphosate-resistant soybean will be planted (500,000 seeds ha^-1) in rows spaced 18 cm apart (drilled). The primary tillage system will be chisel plow. Target weed species will be selected based on the criteria described above.

Target weeds will be established by seed as described above, or by transplanted greenhouse-grown seedlings. Shortly after establishment, weed seedlings will be thinned by hand to a single moderate density (e.g. 10-20 plants m^-2). Target weeds will be established at three or more times, representing three or more cohorts. Time of planting will be based on expected time of weed emergence relative to crop growth stage as described above. Non-target weed species will be managed as described above.

Height and diameter of three or more target weeds will be measured weekly as described above in each sub-subplot throughout the growing season. Above-ground biomass and seed production of each weed species will be determined at or shortly after physiological maturity. Crop plant height, diameter, and growth stage will be recorded weekly. Corn grain yield will be determined by hand harvesting 20 ears from the middle two rows of each plot; length of harvested row will be recorded. Soybean grain yield will be determined by counting, harvesting, and threshing (by hand) all soybean plants within a 1-m^-2 area in each sub-subplot.

Individual Research Projects

Characterization of Chenopodium album and Setaria faberi growth parameters for prediction of Zea mays and Glycine max yield loss (Stoltenberg et al., University of Wisconsin).

Cohort and density effects of Setaria faberi on soybean yield loss (Stoltenberg et al., University of Wisconsin).

Phenology of Chenopodium album growth parameters as influenced by biotic and abiotic factors (Stoltenberg et al., University of Wisconsin).

Photosynthetic plasticity and fecundity of Chenopodium album in variable environments (Stoltenberg et al., University of Wisconsin).

Characterization of Chenopodium album and Setaria faberi interference in Zea mays and in Glycine max (Stoltenberg et al., University of Wisconsin)

Quantify corn and velvetleaf growth, nitrogen uptake, biomass partitioning, canopy structure, and competition as influenced by nitrogen supply. (Lindquist, University of Nebraska)

Quantify corn and velvetleaf leaf area growth rate from emergence to canopy closure as a function of air and soil temperature and solar radiation. Evaluate which environmental variable (or combination thereof) best predicts growth rate among years and locations. (Lindquist, University of Nebraska)

Evaluate the performance of an ecophysiological model for corn-velvetleaf competition for light (INTERCOM) against NC-202 competition data sets. (Lindquist, University of Nebraska)

Predict the potential variation in the relationship between corn yield loss and corn and velvetleaf density and relative time of emergence using 33 years of historical weather data within INTERCOM. (Lindquist, University of Nebraska)

Quantify corn and velvetleaf growth, water use, biomass partitioning, canopy structure, and competition as influenced by water supply. (Lindquist, University of Nebraska - provided external funding can be obtained)

Objective 2. Understand spatial, temporal, and biological variability of weed seed in the soil seedbank and it's impact on weed/crop competition

The seedbank is the source of future weed infestations. Understanding the fate of weed seeds will allow us to manipulate seed fate in agronomic systems and predict the variability in weed seedling emergence. To continue our efforts in understanding the population dynamics of weed seedbanks, we propose three regional objectives as well as the continued support of individual projects focused on the understanding of population dynamics of seedbanks.

Subobjectives:

2A) Conduct a regional protocol assessing the fates of weed seed in the soil seed bank.

The fate of weed seeds in the soil seedbank may be influenced by variability in terrain, soil physical and chemical properties, or by environmental conditions. To study the effect of these variables on seed fate, a series of contained seedbanks consisting of 40 cm PVC pipes placed 10 cm deep into the soil in early August at the time of seed rain. Seeds of 500 velvetleaf, 500 common lambsquarters, and 500 giant foxtail will be 1) placed on the soil surface and 2) mixed within the upper 5 cm of the soil profile within each contained seedbank. NC-202 has conducted research on the competitiveness of these weeds in corn, seed production, and the % emergence. A small wire mesh screen will be placed over some of the PVC pipes to exclude vertebrates/invertebrates from entering the enclosed seedbank area. The remaining PVC pipes will be open to vertebrate/invertebrate predation. Weed seedling emergence will be counted weekly in the fall and twice weekly the following spring and summer. Seedlings will be removed after counting with minimal soil disturbance. Soil will be excavated to a 10 cm depth in each of these PVC pipes at the end of the sampling period, placed in bags and stored at 5 C until the time of seed extraction. Seeds will be enumerated by species and viability assessed using an initial pressure test followed by tetrazolium testing for seed viability. From this experiment, we will be able to determine the percent of weed seed that emerged or remained dormant, as well as the percentage of dead seed. Our comparison of exclosure versus no exclosure will allow us to determine the percent of weed seed that was predated at each field site. This experiment will be conducted in a no-till field so PVC pipe can be placed in the field in August at the time of seed rain and remain in the field through the following growing season. The study will be repeated again the following year.

See attached of Procedures continued.

Measurement of Progress and Results

Outputs

Outcomes or Projected Impacts

• At the 2nd National IPM conference, scientists identified a list of constraints to IPM adoption. High on the list was the development of decision aids for IPM practitioners and improving the reliability of pest induced crop loss estimates. The principle focus of this proposal is to develop a bioeconomic decision aid and to improve our understanding of factors influencing short and long term crop loss estimates. This is a dynamic project that meets 2-3 times each year. Many of the members of the group see the three-day summer meeting as their most substantive meeting of the year. Results of protocol studies have already been applied in bioeconomic models in some states. The research outlined in this proposal will provide the needed knowledge base to parameterize and validate several weed management decision support systems for use across the North Central States. However, these models are restricted to predicting the crop response to weeds from measurement of the seed bank in the early part of the year and not over a series of years. Additional information on the influence of environment on crop loss and an increased understanding of seed bank processes will be necessary to extend the long-term predictive ability and geographic range of the model into the Midwestern corn and soybean growing states.

Milestones

(0):0

Projected Participation

View Appendix E: Participation

Outreach Plan

Organization/Governance

The project will be planned and executed by a Technical Committee composed of one representative from each state. However, each experiment station and federal agency may have more than one representative on the committee where the scope of the project involves more than one subject matter discipline. The administrative advisor and CSRS-USDA representatives are non-voting but retain veto power.


Members of the technical committee of this project will meet annually. In addition to addressing routine business matters, the general purpose and motivating exercise will be to critique the adequacy and effectiveness of established research procedures, to discuss and debate the meaning and interpretation of data, suggest avenues to follow for best objective achievement, and to discuss the regional implication that the research will have. This process is viewed as critical and contributory to the regional research concept.


The officers shall consist of a chairperson, vice chairperson, and secretary. The vice chairperson and secretary will be elected annually, and the vice chairperson shall become chairperson, in consultation with the administrative advisor. The chairperson presides over the Technical Committee and Executive Committee and is responsible for preparing or supervising the preparation of the annual report of the regional project. The Chairperson is also responsible for site selection and coordination of the annual meeting and other meetings held at regional and national discipline meetings. The secretary records and distributes the minutes and performs other duties assigned by the Technical Committee.

Literature Cited

• Buhler, D.D., R.P. King, S.M. Swinton, J.L. Gunsolus, and F. Forcella. 1996. Field evaluation of a bioeconomic model for weed management in corn. Weed Science 44:915-923.


• Buhler, D.D., R.P. King, S.M. Swinton, J.L. Gunsolus, and F. Forcella. 1997. Field evaluation of a bioeconomic model for weed management in soybean. Weed Science 45:158-165.


• Cousens, R., and M. Mortimer. 1995. Dynamics of Weed Populations. Cambridge University Press, Great Britain. 332 pages.


• Forcella F., R.P. King, S.M. Swinton, D.D. Buhler, and J.L. Gunsolus. 1996. Multi-year validation of a decision aid for integrated weed management in row crops. Weed Science 44:650-661.


• Ghersa, C.M., and M.L. Roush. 1993. Searching for solutions to weed problems. BioSci. 43:104-109.


• Hurley, T.M., B.A. Babcock and R.L. Hellmich. 1997. Biotechnology and pest resistance: an economic assessment of refuges. Center for Agricultural and Rural Development, Working Paper 97-WP 183. Ames, IA.


• Johnson, G.A., D.A. Mortensen, L.Y. Young and A.R. Martin. 1995. The stability of weed seedling population models and parameters in eastern Nebraska corn (Zea mays) and soybean (Glycine max) fields. Weed Sci. 43:604-611.


• Jordan, N. 1993. Simulation analysis of weed population dynamics in ridge tilled fields. Weed Sci. 41:468-474.


• Lindquist, J. L. and D. A. Mortensen. 1998. Tolerance and velvetleaf (Abutilon theophrasti) suppressive ability of two old and two modern corn (Zea mays) hybrids. Weed Sci. 46:569-574.


• Lindquist, J.L., B.D. Maxwell, D.D. Buhler, and J.L. Gunsolus. 1995. Modeling the population dynamics and economics of velvetleaf (Abutilon theophrasti) in a corn (Zea mays)-soybean (Glycine max) rotation. Weed Sci. 43:269-275.


• Lindquist, J.L., J.A. Dielman, D.A. Mortensen, G.A. Johnson and D.Y. Wyse-Pester. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Tech. 12:7-13.


• Martin, A.R., D.A. Mortensen, and J.L. Lindquist. 1998. Decision support models for weed management: in-field management tools. In Hatfield, J.L., D.D. Buhler, and B.A. Stewart (eds.), Integrated Weed and Soil Management. Ann Arbor Press, Inc., Chelsea, MI.


• Maxwell, B.D. 1996. The structure and application of bioeconomic models. Proc. North Cent. Weed Sci. Soc. 51:169-170.


• Maxwell, B.D., M.V. Wilson, and S.R. Radosevich. 1988. Population modeling approach for evaluating leafy spurge (Euphorbia esula) development and control. Weed Tech. 132-138.


• Maxwell, B.D., M.L. Roush and S.R. Radosevich. 1990. Prediction the evolution and dynamics of herbicide resistance in weed populations. Weed Tech. 4:2-13.


• Maxwell, B.D. 1992. Weed thresholds: the space component and considerations for herbicide resistance. Weed Tech. 6:205-212.


• Mulugeta, D., and D.E. Stoltenberg. 1997. Weed and seedbank management with integrated methods as influenced by tillage. Weed Sci. 45:706-715.


• Navas, M.L. 1991. Using plant population biology in weed research: A strategy to improve weed management. Weed Res. 31:171-179.


• Oriade, C.A., R.P. King, F. Forcella, and J.L. Gunsolus. 1996. A bioeconomic analysis of site-specific management for weed control. Rev. Agric. Econ. 18: 523-535.


• Swinton, S.M., D.W. Lybecker, and R.P. King. 1995. The effect of local triazine restriction policies on recommended weed management in corn. Rev. Agric. Econ. 17:351-367.


• Wyse, D.L. 1994. New technologies and approaches for weed management in sustainable agricultural systems. Weed Tech. 8:403-407.

Attachments

Land Grant Participating States/Institutions

CO, IA, IL, KS, MI, MN, MO, ND, OH, SD, WA, WI

Non Land Grant Participating States/Institutions