http://www.fao.org/agriculture/crops/thematic-sitemap/theme/spi/en/ Presentation by Joseph P. Messina (Michigan State University) exploring in depth the Farm Input Subsidy Program (FISP) in Malawi through a crop modelling approach. The presentation was delivered in occasion of the “Putting Perennial crops to work in practice” workshop in Bamako, Mali (1-5 September 2015).

1. 9/3/15
1
Joseph
P.
Messina
Ph.D.
Associate
Dean
for
Research
and
Professor
College
of
Social
Science,
Center
for
Global
Change
and
Earth
Observations,
the
Department
of
Geography,
Michigan
State
University.
jpm@msu.edu 517-­‐432-­‐3436
Decadal Satellite Observations
and the Myth of Malawian Farm
Input Subsidy Programme
Farm Input Subsidy Program (FISP) in Malawi
• This widely discussed subsidy program has been praised
in the press as “brilliant” leading to “(doubled
production) within one harvest season” (J. Sachs, NYT
April 19, 2012)
• All popular press sources referred to government
statistics regarding production yields as having
significantly increased and being positive in the near
term for small-holder farmers.
• As Predro Sanchez commented (Nature: 2015) “in spite
of criticisms by donor agencies and academics, the seed
and fertilizer subsidies provided food security to millions
of Malawians.”
• This optimistic assessment of potential for an African
Green Revolution must be tempered by the fact that the
Malawian production miracle is largely a myth.
http://www.faceofmalawi.com/2014/10/donors-­‐demand-­‐
full-­‐details-­‐of-­‐fisp/

2. 9/3/15
2
An
Accidental
Discovery
and
how
I
started
this
process.
• Perennial
grains
to
improve
resilience
to
climate
change.
• Malawi,
Mali,
Ghana,
Tanzania
• Pigeon
Pea
in
Malawi
• Where
are
the
marginal
lands?
1. Agricultural
lands?
2. Production
trends
3. Inter-­‐ vs.
inter-­‐annual
trends
Crop
Models?
• DSSAT,
SALUS,
APSIM,
…
• Scale
• Data
uncertainty

3. 9/3/15
3
Yield?
• Yield
=
f(edaphic
*
climate
*
crop
*
social)
• Multiscalarprocesses
• Soils
are
local
but
also
a
product
of
watershed
dynamics
• Climate
is
global
but
also
a
local
physical
process
• Social
is
endogenous
(labor,
capital,…)
and
exogenous
(FISP,
market
prices,
trade,
…)
• Agronomy
is
local
but
also
responsive
across
scale
• Food
security
scales
from
local
to
national.
Global
Change
Biology.
doi:
10.1111/gcb.12838
Uncertainty & Social vs. Biophysical Drivers
• CRU trend versus ERA40 trend in
rainfall.
• Blue=wetter trend,
• Orange = drying trend.
• Different spatial scales and process-based
methods that use THE SAME STATION
DATA lead to drastically different outcomes
based mainly on scale (i.e. where blue and
orange overlap).
• So, How do we fix this problem?

4. 9/3/15
4
Where
are
the
marginal
agricultural
lands?
Mozambique
Tanzania
Zimbabwe
Zambia
Agricultural Land Classification
Disagreement in Malawi
Coverage of Agricultural Land
FAO 2010 & IFPRI 2002 - 35%
IFPRI 2002 Only - 26%
FAO 2010 Only - 16%
Non Ag
0 10050
Kilometers
• What
does
this
even
mean?
• Standard
LULC
products
answer
the
wrong
question
(How
much?)
and
they
try
to
minimize
overall
classification
error
• I
need
to
minimize
errors
of
commission.
– a
very
reliable
sample…
• Solution:
use
all
LULC
products

5. 9/3/15
5
RS
based
LAI
vs.
SALUS
Crop
Model
But
where
is
the
FISP
bump???
Soils
and
climate
and
other
things
we
need
to
distinguish
• Marginal
lands
– relative
terms
• Soils
– need
to
extract
soil
drivers
• Climate
– inter
and
intra
annual
variability
• Scale
– local
heterogeneity
is
likely
a
social
process

6. 9/3/15
6
Factors:
1. Slope
2. Soil erosion hazard
3. Soil bulk density
4. Soil organic matter
5. Soil cation exchange capacity
6. Soil texture
7. Soil pH
8. Soil drainage
9. Soil depth
Methods:
Average of
1. Geometric mean
2. Rabia
3. Square root
4. Storie
5. Weighted sum
Malawi
Agricultural
Land
Suitability
Categories Area (ha) Percentage
Highly suitable 915431 7.8
Moderately suitable 2458882 20.8
Marginal suitable 2379508 20.1
Suitable land total 5753821 48.7
Poorly suitable 1081160 9.2
Permanently unsuitable 2496676 21.1
Unsuitable land total 3577836 30.3
Land subtotal 9331657 79.0
Water 2479429 21.0
Malawi total area 11811086 100.0

7. 9/3/15
7
Optimal
Pigeon
Pea
• Niche
generation
• Extensive
data
– but
a
manageable
process
• Organized
by
management
unit
to
facilitate
scaling
and
adoption
• But,
• Sensitivity
to
climate?
• Variability
across
scales?
• This
is
not
substantially
different
than
a
well
parameterized
crop
model

8. 9/3/15
8
Production
trends
• Relative
terms
• Malawi
specific
scale
• Sensitive
• High
inter-­‐annual
variability
• Missing?
• Scaled
trends
• Sources
of
the
variability

9. 9/3/15
9
Rainfall
and
Productivity
Trends
on
Agricultural
Land
in
Malawi

10. 9/3/15
10
Now
what?
• We
can
improve
targeting.
• Biophysical
solutions
need
to
target
biophysical
problems
• Social
solutions
…
• Adoption
is
cultural
• Next
steps?
Climate
&
Changing
Seasons

11. 9/3/15
11
FISP
comments
• There
are
multiple
lines
of
evidence
that
on
many
farms
soil
organic
matter
status
has
degraded
to
a
level
that
no
longer
support’s
maize
growth
or
responsiveness
to
fertilizer.
• No
clear
trends
of
improvement
(Dorward et
al.,
2010a).
• The
incremental
production
estimates
are,
however,
considerably
lower
than
those
implicit
in
the
national
crop
estimates
for
maize
production,
with
much
lower
variation.
(Dorwardet
al.,
2010a).
• Annual
changes
in
maize
prices
also
suggest
that
post-­‐subsidy
maize
supplies
have
been
lower
than
suggestedby
the
national
crop
estimates.
(Dorwardand
Chirwa,
2011)
• “It
is
widely
believed
that
the
2007
Malawi
harvest
was
overestimated
by
at
least
25%. If
the
government
had
been
able
to
produce
a
more
accurate
estimate
of
crop
production,
it
might
not
have
arranged
to
export
maize,
which
in
turn
might
have
avoided
the
huge
price
surge
in
late
2007/early
2008
which
caused
great
hardship
for
maize
buying
households.”
(Jayne,
2008)

12. 9/3/15
12
The
development
orthodoxy
on
agricultural
• Intensification
(all
the
time)
• Better
varieties
• Better
supply
chains
• Better
subsidies
• Scaling
matters
• A
function
of
targeting
• The
Climate
Change
Quandary
• The
new
varieties
may
not
be
appropriate
• Tools
• Adoption
potential
• Unintended
Consequences
• Disease
– livestock,
plant,
and
human
• Climate
and
Ag
feedbacks
• Big
Data
solutions
will
help
solve
some
of
the
scaling,
climate,
and
targeting
challenges
faced
by
the
development
community.

13. 9/3/15
13
Funding Provided by:
• The Bill & Melinda Gates Foundation
• USAID – Global Development Lab & GCFSI
Thank You / Questions
Reference: Decadal Satellite Observations and the Myth of Malawian Farm Input Subsidy
Programme. 2015. Messina, J. Peter, B. Li, G. DeVisser, M. Snapp, S. Moore, N. Nejadhashemi, P.
Putting Perennial crops to work in practice: Pigeonpeas and Sorghum. Bamako, Mali