METs for evaluating experimental varieties.

Resp

onse

var

iabl

e:G

rain

yie

ld

low moderate

A

B

extreme

Basics of Genotype x Environment interaction

Context: eg Drought stress in the target area

Extent of GxE depends on range of E covered, choices of G included and type of response variable

C

Resp

onse

var

iabl

e:G

rain

yie

ld

low moderate

A

B

extreme

G x E design problem:

Context: Drought stress in the target area

C

Collect data to estimate responses, being• Efficient• Valid

The ‘classical’ Multi-Environment Trial for GxE16 G (4 reps)

(Randomised block design) 8 E

Measure responses

The variety diversity to explore.- How many? - Which ones? - Who decides?

Sampling the variability in project domain-Which dimensions?How many?Who decides?

Set by breeding objectives - multiple

Design for efficiency, validity, practicality,legitimacy

GxE → OxC

Genotype

Climate

Soil

Management

Farm resource endowment

Market integration

Gender, HH type

Environment Yield

Growth traits

Disease resistance

Profitability

Acceptability

Preferences

X =

Context PerformanceX =

=

Design principles?

Genotype

Objectives determine design

A. Objectives require average performance across environments• GxE is part of the ‘noise’• Random selection of E to ‘represent’ target• Number needed depends on σ2

ge

B. Objectives require detection, description and understanding of GxE

• Include hypotheses of GxC interactions• Essential for designing efficient trials

Predictable

Unpredictable

Mappable

Factors hypothesised to interact with G that you want to investigate

- They have to vary within the study!

Some can be manipulated or chosen for any plot:• Planting date• Fertilizer use• Intercrop• …

Some can not:• Soil type• AEZ• Farmer resource level• Farmer gender• Landscape niche• Weather• Pest pressure• …

Can factor be manipulated

?

Will you include it as an

experimental factor?

Definition of treatments

Design choices

Yes Yes

No No

Objectives of G x C

hypotheses

C factors to investigate

Business as usual

Yes No

Type of C factor

Location StrataRandom sampling

Replication

Designchoices

PredicatableMappable

PredicatableNon-mappable

Unpredictable

Do the trialMeasure context variables

Data analysisIncluding

Consistency across farms

Unexplained variation = risk

is small enough?

New hypothese of G x C

Carry on!

Yes

No

Do the trial with farmers because…

1. Assessing under realistic conditions and contexts

2. Measuring preferences3. Sampling sufficient context variation4. Making required large-N trials feasible5. Participatory principles, empowerment and

farmers rights

1m 0.5m

What might change when farmers are involved?Layout

Researchers design Farmers and researchers design

Farm 1 Farm 2

Farm 15

Experiment spread across many farms

Layout may not be ‘neat’!

See the videos!

https://www.youtube.com/watch?v=ItLyRW2LaAQ

4 on design of trials with farmers1 on design of METs

Malawi example

What it might look like

Alt gradient covers 4 AEZs

About 20 Farmer groups

- 2 main types

About 20 farms per group of several

types

Each farm –bean field with

2-6 test plots

Planted, managed, measured by farmers

About 400 farms and 1600 plots = Large N?

Farms as Environments (Contexts)

Deviation from straight line = GxE+residual

GxE as risk for farmers

Residual is simply more

GxE

G x E G x C

G x E

Location Mega-environments (Mechanisms)

Recommendation domainsMaps

G x C

Location + situation

Variety Characteristics Adaptation

Choices(Mechanisms)

Questions

• What works well and what needs to change in the way you do METs?

• What are the complexities and questions you face in designing and implementing METs?