Project Overview
Annual Reports
Commodities
- Agronomic: grass (misc. perennial), hay
Practices
- Animal Production: feed/forage
- Crop Production: nutrient cycling
- Education and Training: decision support system, demonstration, extension, on-farm/ranch research, participatory research, technical assistance
- Farm Business Management: budgets/cost and returns, agricultural finance
- Pest Management: chemical control, competition, economic threshold, field monitoring/scouting, weather monitoring, weed ecology
- Production Systems: agroecosystems, holistic management
- Soil Management: soil analysis, nutrient mineralization
Proposal abstract:
Ventenata (Ventenata dubia; North Africa grass) is an invasive winter-annual grass of increasing concern to the Inland Northwest; yet little research has been conducted on its biology and management. Significant economic and ecological harm have occurred in perennial grass production systems, including grass-hay, pasture and Conservation Reserve Program (CRP) land. We propose to conduct field studies to inform and improve the decision-making process of a ventenata IPM program in grass production systems of the Inland Northwest. Research objectives include using a degree-day model approach to predicting ventenata life-cycle stages, defining the impact of ventenata on farm nitrogen-budgets and determining crop responses to integrated control strategies. Stakeholder inputs and research results will be incorporated into a web-based Decision Support Tool (DST) that provides system-specific recommendations for improved ventenata management decision-making.
Project objectives from proposal:
Education
EO: Involve stakeholders in production of ventenata IPM using a web-based, decision support tool (DST).
Performance Target (PT): Measure producer adoption rates and economic impacts via pre/post survey.
DSTs are effective educational and management tools for aiding weed management decisions. DSTs function well when predictions result in efficient decisions and are accessible and easy to use. Several weed
DSTs are on the internet, which provides a linkage to the user, enables measurement of adoption rates and allows for feedback.
Research
RO: Conduct studies to improve ventenata IPM decision-making.
PT: Value of the information from ecological studies will be measured in pre/post surveys for the DST. The goal of IPM is to maximize profit margins, safeguard natural resources and minimize negative impacts on the environment. In the context of weed management, IPM emphasizes a knowledge-based system using multiple control strategies designed to promote crop competitive ability and reduce cost of inputs such as herbicide use.
RO1: Predict ventenata seed germination and seed set using a degree-day model approach.
PT: Test model significance and distribute via DST. Understanding weed phenology aids development of control strategies targeted to stage of plant development. Degree day models have been used to schedule cultural and chemical controls for grasses like downy brome.
RO2: Define impact of ventenata within a whole-farm system nitrogen (N) budget.
PT: Produce region and production-system specific N-budgets at a range of ventenata population levels and distribute via DST. The use of N-budgets can be a useful tool for predicting the impact of N-management practices on crop competitiveness, weed control and N-use efficiency. N-budgets can also be used within
bio-economic decision support tools.
RO3: Determine crop competitiveness (yield) response to alternative management strategies.
PT: Identify minimum management inputs required to control ventenata and distribute via DST. Increasing crop competitiveness is one aspect of IPM that reduces pesticide inputs. Crop competitiveness depends on
forage species selection and timing of operations such as harvest, pesticide or cultural controls. Determining the effect of initial site conditions is critical for minimizing management inputs. A prior study suggested fertilizer alone increased perennial grass cover regardless of the level of ventenata. Studies will be conducted within a DST framework, by measuring yield across a hierarchy of management strategies and conditions.