Transcript Performance of agroforestry systems under future climate

Performance of
agroforestry systems
under future climate:
hypotheses and methods
a dna ™emiTk ciuQ
ro sse rpmo ced )de s serpmo cnU ( FFIT
.erut cip sih t ee s ot dedeen era
Amber Kerr
Energy and Resources Group
February 26, 2008
Talk outline
Recap: Dissertation topic and goals
Why and how am I going to carry out my...
• Field work
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– Hypotheses and questions
– Logistics, data, materials
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Simulation modeling
Meta-analysis
Slide 1 of 23
I will test the overall hypothesis:
Under future climate, agroforestry
systems will continue to outperform
maize monoculture, but their yield
advantage will be diminished due to
belowground competition for water.
Slide 2 of 23
If possible, I will also ask:
WHY does performance change (or
not)?
• Is transpirational demand different
between the systems?
• Are there differences in soil moisture?
• Do rooting zones largely overlap, implying
competition for belowground resources?
• Do nutrient limitations arise as a result of
Slide 3 of 23
water limitations?
Five clarifications needed...
What aspects of climate will be
manipulated? How?
What types of
agroforestry systems will
be compared?
Under future climate, agroforestry
systems will continue to outperform
maize monoculture, but their yield
advantage will be diminished due to
belowground competition for water.
What is the
appropriate
control?
How will “performance” will be measured?
What about minima and variances?
How will the existence of
competition be deduced?
Slide 4 of 23
1. Future climate
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I believe that rainfall manipulations
are the most practical and most
interesting approach to simulating
future climate.
They have two major advantages:
– Novelty (never before done on an
agroforestry system);
– Direct testing of competition hypothesis
(otherwise must be answered indirectly).
Slide 5 of 23
Encouragement from ICRAF
“I think rainfall manipulations are particularly
valuable, particularly in Southern Africa. You
should be able to build relatively inexpensive
exclusions out of local materials. We’re not
talking about something on the order of what
Dan Nepstad did in the Amazon; I think if you
aim for a few areas of 10 x 10 m, you’ll be fine.”
~ Louis Verchot, ICRAF Principal Scientist
(e-mail 2/25/08)
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Low-tech, complete exclusion
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Without a major grant, I won’t be able
to build the sturdy adjustable rainout
shelters used by Dawson and colleagues.
•
Instead, I will build
simple structures to
exclude all precipitation,
and remove them
D. Nepstad’s Amazon
intermittently.
experiment (photo: WHRC)
Slide 7 of 23
Magnitude of manipulation
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To ensure I see a treatment effect, and to
lessen the risk of one or two very wet
years, I will aim for a level of rainfall
exclusion that is more severe than
expected under average future climate.
I could also aim to alter the timing of
precipitation (harsher curtailment early in
the growing season).
Slide 8 of 23
Rainfall: unresolved questions
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On what time schedule should I put the
rainout shelters in place?
– 50% of the time?
– Every other storm?
– More than one different type of manipulation?
•
What if the shelters don’t work?
– Water addition?
– Look at interannual variability?
– Examine different spatial arrangements?
Slide 9 of 23
2. Agroforestry systems
In an ideal world, I would consider
• Multiple systems:
– Hedgerow intercropping
– Relay intercropping
– Improved fallows
Multiple agroforestry species
But budget is unlikely to permit this, so...
•
Slide 10 of 23
Agroforestry systems, continued
...I will consider only one type of
agroforestry system, either
• Relay intercropping, or
• Hedgerow intercropping
(depending on whether I have access to
established plots).
Unfortunately this will mean I cannot
compare different types of agroforestry.
Slide 11 of 23
3. Controls
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In an ideal world, I would include multiple
controls to test different mechanistic hypotheses:
– Monoculture maize
• Fertilized
• Unfertilized
– Monoculture tree
– Annual legume intercrop
•
Due to resource constraints, probably only one
control will be possible (monoculture maize).
Slide 12 of 23
90 cm
between
rows
90 cm
within
rows
M = maize
T = tree
(absent in
controls)
Whole plot:
9 x 9 grid,
(7.2 m)2,
81 indivs
per species
M
T
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Plot design
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Furrow
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Ridge
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Sampled
subplot:
5 x 6 grid,
(4 m)2,
30 indivs
per species
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Planting density of controls
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Depending on the question you wish to
test, the appropriate monoculture
comparison could either be planted at
– The same total density as the intercrop; or
– The same conspecific density as the intercrop.
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Since I wish to test the options actually
faced by farmers, I will choose the
latter configuration.
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The problem of combinatorics
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(3 treatments) + (3 controls)
3 levels of precipitation
2 levels of nitrogen
6 replicates per treatment
... 6 * 3 * 2 * 6 = 216 plots! More realistic:
(1 treatment) + (1 control)
2 levels of precipitation
4 replicates per treatment
... 2 * 2 * 4 = 16 plots.
Slide 15 of 23
4. Metrics of performance
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The single most important variable to
measure is maize yield. This is the
bottom line for farmers.
Also useful (but optional) would be:
– Total maize biomass, including stover
– Total tree biomass, separated into leafy and
woody components (ideally roots too!)
Slide 16 of 23
Yield variability
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Farmers need to worry not only about
average yield, but also minimum yield
(and, in general, yield variability).
I will not be able to test this with two
years of field data.
Instead, I will attempt to address this
issue indirectly through meta-analysis
and simulation modeling.
Slide 17 of 23
5. Measuring competition
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The advantage of the rainfall exclusion
is that it will allow me to say:
If reduced precipitation has a greater
negative effect on maize yield when
maize is intercropped rather than
grown as a monoculture, then the
maize must be competing for water in
the intercrop.
Slide 18 of 23
Measuring competition, cont.
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Comparing yields should be adequate to
determine the existence of competition,
it would be better to also have data on
– Root distribution
– Soil moisture
– Total water use
– (perhaps) Water use efficiency
...to explain why competition was (or
wasn’t) present in the agroforestry plots.
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Simulation modeling
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Simulation modeling will allow me to:
Examine more aspects of climate change
(including temperature) on a wider range of
systems over longer time scales.
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Simulating actual yields is not a goal.
Improving /creating a model may be a goal.
Progress: Am learning how to use
WaNuLCAS and corresponding with its
creators; also considering different models.
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Meta-analysis
Three goals for my review of existing data:
1. Refine the questions and methods for
my fieldwork.
2. Complement (and provide a reality
check for) simulation model output.
3. Write a review article on the use of
agroforestry for adaptation to climate
change.
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What do you think?
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Will it still be useful to look at only at
one type of agroforestry system?
Beyond yield, which measurements
should I prioritize? (Which
mechanisms are potentially most
interesting and important?)
Is the scope of this work manageable?
Have I provided a realistic budget?
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Thank you very much!
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In addition to everyone who helped me prepare
for my qualifying exam, I would like to thank:
Margaret Torn, for frequent conversations
Lou Verchot, for big-picture advice
Paxie Chirwa and Colin Black, for sharing data
from Makoka Research Station
Ni’matul Khasanah, for troubleshooting help with
WaNuLCAS
John Harte, Alex Farrell, Sintana Vergara, Derek
Lemoine, Mike Kiparsky, Kevin Fingerman, and
Adam Smith, for feedback on an earlier talk
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