Wednesday, April 25, 2007

Systems Research: Challenges and Oppurtunities

Managing complexity in systems research

i. Managing multiple perspectives

ii. Identify windows of interest rather than boundaries

iii. Changing systems to maintain multiple perspectives

iv. Mechanistic tools may not fit

Balancing Farming Integrity and Research Needs in Simulated Cropping Systems
i. Address both ecological and economic problems/ concerns

ii. Reward structure should balance quick success with long-term benefits

iii. Perform treatments on farm-relevant scales

iv. Communicate cross disciplinary approaches with example applications

v. Embrace a dynamic research model (e.g. crop sequences)

Keeping the Research Components Integrated During the Implementation Phase

i. Need a vision keeper, group capacity builder

ii. Publish process as well as research

iii. Pursue adaptive management research

iv. Need to share common vision, or commitment to solve a problem

v. Forge shared hypothesis, detailed objectives

Including Farmers as Equal Collaborators While Also Being Efficient in Using Their Time

i. Offer fair compensation for farmer’s time

ii. Clearly outline goals, objectives, and expectations

iii. Develop a project so farmers have vested interest

iv. Develop mutual respect and honor farmer knowledge

v. Respect farmer time constraints during the growing season.

Reducing the time required for meetings in interdisciplinary research

i. Use visual tools such as concept mapping

ii. Use highly qualified and motivated research staff

iii. Invest in human capacity, such as a good facilitator

iv. find the right time for collaborators

v. empower farmer and students to take responsibility for the agenda

Melding disciplines within a systems level research project

i. Encourage more merging of disciplines in school so that new faculty are accustomed to this process

ii. Since you need multiple perspectives to understand whole systems each discipline should take the time to appreciate the strengths of all the other disciplines.

Leaving Behind the Factorial Mentality in Designing Systems Experiments

i. Don’t forget factorials but use them wisely. Consider starting with systems then working backwards. Pulling out the smaller questions to use in factorials

ii. Need new sources of funding. Current examples include SARE and NRCS Innovation Grants

iii. Need alternate publications to support systems research

iv. Appreciate the synergy of systems/ narrative as science. There is still a need to use regular quantitative analysis.

Building Flexibility into Systems Level Research

i. Put a lot of time and focus into initial design and principles. If you stick to these principles, and they are strong, then fluidity will be accepted.

ii. Build on an idea of adaptive metrics (which tell us how well we are responding to the challenges presented to us by our environments.

7 comments:

Anonymous said...

Perform objective tests that evaluate the quality of organic food (i.e. is it more valuable or not) and publish these objective tests.

Anonymous said...

Produce a textbook on how to conduct systems research and present case studies that were successful !!!!

Anonymous said...

Comment on Managing Complexity in Systems Research

Use clear language [when communicating research concepts and experimental findings]. Don’t get bogged down by semantics.

Anonymous said...

Comment on “Including Farmers as Equal Collaborators While Also Being Efficient in Using Their Time”

Farmers are our audience, bringing them into the picture will increase the relevance of [our] research and allow for a better sense of what best management practices (BMPs) are economic viable.

Anonymous said...

Comment on "Reducing the time required for meetings in interdisciplinary research"

Include technical writers in funding requests. Set framework for publication based on a definition of areas of expertise

Anonymous said...

Comment on “Melding disciplines within a systems level research project”

Always ensure that your experiments have sufficient treatment levels to prove the primary hypothesis. Treatment replication (min. 3) and experimental repetition (min. 2) should always be used!!!! You should not abandon the factorial approach! It is essential to convey accurate and compelling statistics in order to ensure that time is not wasted repeating something that should have been done in the first place. Many regulatory authorities require this kind of information in order to fully support a product or endorse something.

Anonymous said...

Comment on “Building flexibility into systems level research”

If the treatment factor is a systems-level factor, then a systems experiment reduces (or can reduce) to a standard factional design then, present it that way.