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A MANITOBA PERSPECTIVEOn-Farm Research Across Time and Space
Megan Bourns
On-Farm Network AgronomistManitoba Pulse and Soybean Growers
Outline
Evolving into the future
Into the Research
Overview: MPSG’s OFN
On-farm research –why do we care?
Why On-Farm Research?
Agronomic recommendations are generated from small plot research
• Limited seasons of data• Limited number of
locations • BUT very rigorous and
intensive data collection/analyses
Limited time and space
Why On-Farm Research?
On-farm research assesses outcomes across time and space
• Greater number of sites • Larger geographic range• Can be conducted over
several years• BUT limits to intensity of
data collectionOFN soybean fungicide trials 2012-2019
Identifying patterns and probability of response
require more sites in more places
Reality: each field is unique
*This drives the need for on-farm research
Why On-Farm Research?
Small plots: strive for uniformity
On-farm trials: encompassvariability or go within variability
At what scale are management decisions made?
Variability: A Field Scale Reality
The scale of your question should reflect the scale at which management decisions are made
Is the producer treating the field as one
area?
Is the producer treating
management zones
differently?To date, this is how the OFN has approached asking and answering questions
MPSG’s On-Farm Journey
• MPSG began funding on-farm research in 2010
• OFN was officially launched in April 2014
• 335 trials to date
WHAT:
Network of on-farm pulse and soybean research
Fully funded & directed by MB pulse and soybean growers
GOAL:
Test new products & practices for pulse and soybean
Straightforward, reliable research
By farmers, on their farms
Conducted on-farm, with farmersParticipatory
Produces data that are unbiased, accurate and robustPrecise
Results guide management decisions, improve productivity and profitability of the farm operation
Proactive
3 Key Principles:
Dataset Builders:
Common question Treatments
applicable to multiple operations
Intended to be combined across time and space
One-offs:
Very specific to one farm operation
Treatments not consistent across operations
Data from each trial stands alone
Trial Classifications
Ex. Soybean fungicide efficacy trials
Ex. Dry bean tillage system trial
Trial Selection
• Trial ideas develop from: Observations Questions from
producers/agronomists Discussions with
producers/agronomists• 335 trials to date 11 different types of
questions 4 different crops Range of dataset size
• Inform grower decision at a farm level
On-Farm Network Research Outcomes
• Inform grower decision at a farm level
• Inform management recommendations at a regional level
On-Farm Network Research Outcomes
• Inform grower decision at a farm level
• Inform management recommendations at a regional level
• Investigate patterns and probabilities of response across time and space
OFN soybean fungicide trials 2012-2019
On-Farm Network Research Outcomes
Into the Research
Explore these trial types in more detail
New in 2020: tillage system trial, dry bean
Getting More from On-Farm TrialsSOYBEAN ROLLING TRIALS
OBJECTIVE: quantify the agronomic and economic impacts of soybean rolling on non-stony fields
PARTICIPANT GROUPS: MPSG, PAMI, U of M, AAFC
RolledUnrolled
Getting More from On-Farm TrialsSOYBEAN ROLLING TRIALS
MPSG U of M PAMI AAFC
Surface roughness scanning
Cost/economic
gain of rolling
Sediment traps,
modelling
Facilitate trial setup and
harvest
Evaluate agronomics and economics of rolling non-stony land
• Have a slightly different focus, not a true replicated strip trial
• Difficult to get producers to leave multiple unrolled strips
• Even if they did…would rolled vs unrolled yield data alone really give us the best picture?
Getting More from On-Farm TrialsSOYBEAN ROLLING TRIALS
• Sediment movement, surface roughness & economics of rolling are important considerations as well get this through collaboration
• On-farm trials don’t always need to be the simplest form of investigation there is room to do more, to get more
Getting More from On-Farm TrialsSOYBEAN ROLLING TRIALS
On-Farm Trials: Adaptive Science
• Practical research to answer practical questions
• With on-farm trials, you can adapt your science perhaps more than in small plot research
• Room for creativity as long as scientific principles remain sound
Sound Scientific Principles
Reliable Statistical Analysis
Meaningful Results
Adaptive Science: An ExampleStrip-till vs. Conventional Till – Dry Beans
What’s your question?• Comparing tillage? fertility the same, tillage different• Comparing fertility? tillage the same, fertility different…OR…• Comparing systems? fertility & tillage as a package
Strip-till
Banding
Conventional Till
Broadcasting
Adaptive Science: An Example
Rep 5
Rep 6
Rep 3
Rep 4
Rep 1
Rep 2
Standard Trial Layout
• Randomization• Replication • Cover large field area
Comparing tillage systems:• Plan was to follow this
“traditional” randomized, replicated layout
…BUT…
• Equipment widths presented a problem
Modified Trial Layout• Randomized• Replication • Cover large field area
Going Deeper into the DataSOYBEAN SEED TREATMENT TRIALS
OBJECTIVE: quantify the agronomic impacts of seed treatment on soybean
Going Deeper into the DataSOYBEAN SEED TREATMENT TRIALS
OBJECTIVE: quantify the agronomic impacts of seed treatment on soybean
YIELD Disease pressure Insect pressure
Going Deeper into the DataSOYBEAN SEED TREATMENT TRIALS
2.3 2 1.75.3 -1.4
-0.6 -0.2-2.1
-0.12.6
0.9
0.5
-0.8
0.4-0.7
-0.30.1 0.3
0.3
0.00.9 -0.4
0.2 -0.80.6 2.8
2.6
2.3
-0.1
0.4-0.2
-1.6
0.3-0.2
0.7
0.5
0
10
20
30
40
50
60
70
15SS
T01
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T09
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Yiel
d (b
u/ac
)
Sum of Treated - 2015 Sum of Treated - 2016 Sum of Treated - 2017Sum of Treated - 2018 Sum of Untreated - 2015 Sum of Untreated - 2016
*
**
* * * * *
*
What is driving the yield differences?
Going Deeper into the DataSOYBEAN SEED TREATMENT TRIALS
Treated
Untreated
• Are there yield differences? What is driving those differences (or lack of differences)?
• Testing the efficacy of a seed treatment … were there pests there to begin with?
• Scouted for insects and diseases this year Assessing presence/severity of
diseases in the lab (U of M)
Going Deeper into the DataFUNGICIDE TRIALS
• Testing the efficacy of a fungicide treatment on soybean, pea, dry bean
• What is disease pressure like?
• SCALE issue focus your data collection Sample from a
representative transect
Grower-First ResearchINOCULANT TRIALS
OBJECTIVES: Single vs. Double: quantify the agronomic impacts of single inoculant (seed-applied) compared to double inoculant (seed-applied + in-furrow) in soybean
*minimum 2-year soybean history
Single vs. None: quantify the agronomic impacts of single inoculant compared with no inoculant in soybean
*minimum 3-year soybean history
New in 2019: single vs. none in dry bean
Grower-First ResearchINOCULANT TRIALS
Single vs. Double:(min. 2-year soybean history)
• 2 out of 35 site-years with yield response
Single vs. None:(min. 3-year soybean history)
• 0 out of 29 site-years with yield response
Grower-First ResearchINOCULANT TRIALS
Dry beans:Single vs. None
• No yield difference• No difference in
nodule number, position or dry bean growth/vigour
Grower-First ResearchINOCULANT TRIALS
• This has helped develop the check-list for single inoculation of soybean• Now would like to
develop one for no inoculation
• Eventually, develop one for dry beans?
Into the Research: Takeaways
Getting more from on-farm trials Collaboration facilitates a well-rounded story
On-farm trials are adaptive science
Ask practical questions, adapt scientific approach to find practical solutions
Deeper into on-farm trial data
Opportunity to be selectively intensive in data collection
Grower-first research
Finding answers for producer-centric questions
Evolving into the Future• Continue asking “WHY?”• More fully explain the
results of our hypotheses Collaboration Interdisciplinary
approach Selective intensification
of data collection
• Develop resources to enable producers to conduct quality on-farm research independently
Challenges & Opportunities
Challenges
Producer engagement
Data intensification: HOW & WHERE
Facilitating collaborations
Opportunities
Expand utility and value of on-farm data
Engage interdisciplinary collaboration
Investigate patterns & probabilities
OFN Database
OFN Database
Questions?
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