On-Farm Research Across Time and Space · Going Deeper into the Data. SOYBEAN SEED TREATMENT...

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

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

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

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

Network of on-farm pulse and soybean research

Fully funded & directed by MB pulse and soybean growers

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 management recommendations at a regional level

• Investigate patterns and probabilities of response across time and space

OFN soybean fungicide trials 2012-2019

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

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?

• 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

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

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

YIELD Disease pressure Insect pressure

2.3 2 1.75.3 -1.4

-0.6 -0.2-2.1

-0.12.6

0.4-0.7

-0.30.1 0.3

0.00.9 -0.4

0.2 -0.80.6 2.8

0.4-0.2

0.3-0.2

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?

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

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

Dry beans:Single vs. None

• No yield difference• No difference in

nodule number, position or dry bean growth/vigour

• 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

Questions?

On-Farm Research Across Time and Space · Going Deeper into the Data. SOYBEAN SEED TREATMENT...

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