Soil Health in Southern Africa and implications on sustainable intensification: How much is the gap? World Congress Soil Science 2014 L Desta et al

1. Lulseged
Tene1*,
Andrew
Sila2,
Job
Kihara3,
Gi<
Ndengu1,
Powell
Mponela1,
Keith
Shepherd2,
Markus
Walsh4
and
Deborah
Bossio3
www.ciat.cgiar.org
Eco-­‐Efficient
Agriculture
for
the
Poor
Soil
health
in
Southern
Africa
and
implica3on
on
sustainable
intensifica3on:
how
much
is
the
gap?
Lulseged
Tamene1,
Andrew
Sila2,
Job
Kihara1,
Gi<
Ndengu1,
Powell
Mponela1,
Keith
Shepherd2,
Markus
Walsh3
and
Deborah
Bossio1
1CInternaLonal
Center
for
Tropical
Agriculture,
2World
Agroforestry
Center,
3CThe
Earth
InsLtute
20th
World
Congress
of
Soil
Science
June
8-­‐13,
2014,
Jeju.
Korea

2. Source:
INED
u SSA
countries
will
grow
the
fastest
–
pressure
on
resources
Background
PopulaLon
increase

3. Background
This
will
lead
to:
-­‐
ReducLon
in
farm
size
-­‐ Expansion
to
steep
slopes
and
marginal
areas
-­‐ More
degradaLon
-­‐ Low
yield
There
is
a
need
to
produce
more
from
small
areas
Sustainable
IntensificaLon

4. Input
(type,
diversity)
ProducLvity
and
yield
Cost
of
input
Risk
Background
In
areas
low
income
households
and
high
environmental
risk,
implemenLng
sustainable
intensificaLon
may
be
a
challenge

5. Evaluate
soil
nutrient
deficiency
levels
Assess
soil
health
status
and
its
spaLal
variability
Gain
preliminary
picture
of
the
‘gap’
that
should
be
replenished
to
improve
soil
health
and
achieve
“sustainable
intensificaLon”
Suggest
possible
opLons
to
enhance
sustainable
intensificaLon
under
prevailing
SSA
smallholder
circumstances
Objec3ves

6. Study
Area
and
Sites
AfSIS
CRP1.1
Africa
RISING
v 6
Countries
v 29
Sites
v 464
Clusters
v 4640
Plots
v 18560
Sub-­‐plots
v 9280
soil
samples
Study
focuses
in
six
countries
of
Southern
Africa

7. • Take
advantage
of
data
collected
for
different
projects
across
southern
Africa
• Soil
and
landscape
aeributes
data
collected
based
on
spaLally
straLfied
random
sampling
approach
Approaches
Sub-plot = 0.01 ha
Site = 100 km2
Cluster = 1 km2
Plot = 0.1 ha
Sub-plot = 0.01 ha
Site = 100 km2
Cluster = 1 km2
Plot = 0.1 ha
0.01
ha
Site = 100 km2
Cluster = 1 km2
Site = 100 km2
Cluster = 1 km2
Plot = 0.1 ha
Sub-plot=0.01ha
Site=100km2
Cluster=1km2
Plot=0.1ha
Sub-plot=0.01ha
Site=100km2
Cluster=1km2
Plot=0.1ha

8. Figure
(a)
Near-­‐infrared
spectrometer
and
(b)
raw
and
derivaLve
spectra
used
to
calibrate
predicLon
models
Approaches
² Over
9000
top-­‐
an
sub-­‐
soil
samples
² NIR/MIR
spectral
analysis
² 10-­‐20%
wet
chemistry
data
for
calibraLon

9. Results
(%)
(mgkg-­‐1)
(mgkg-­‐1)
(mgkg-­‐1)
(gkg-­‐1)
(CmolcKg-­‐1)

10. The
majority
of
the
sites
experience
low
soil
nutrient
content
ü All
sites
have
zinc
limitaLon
ü About
95%
of
the
sites
suffer
from
nitrogen
deficiency
ü 70%
of
the
sites
are
phosphorus
deficient
ü 65%
of
soils
have
poor
soil
structure
ü About
40%
of
the
sites
have
low
potassium
level
ü About
40%
of
the
sites
have
poor
SOC
q Overall
soil
ferLlity
status
is
beeer
in
Malawi
Results:
overall
soil
condiLon

11. Total
N
(%)
Results:
Prevalence
es3mates
and
cri3cal
limits

12. Results:
Prevalence
es3mates
and
cri3cal
limits
ApplicaLon
of
P
is
most
of
Malawian
soils
may
not
be
effecLve
–
even
it
may
lead
to
environmental
risk
due
to
P
leaching
into
water
bodies?
P
(mg
kg-­‐1)

13. Results:
spa3al
variability
within
&
between
countries
&
sites
In
addiLon
to
the
observed
gap,
there
spaLal
variability
at
site,
cluster,
plot
levels
Example
K
for
one
site
and
cluster
in
Botswana
Country:
Botswana
Site:
Shoshong
Country:
Botswana
Site:
Shoshong
Cluster:
9
(variability
within
a
cluster,
between
plots
Cluster
Plot
ImplicaLon
on
ferLlizer
recommendaLon:
fine-­‐tune
to
local
soil
condiLons

14. EsLmate
gap
between
current
soil
nutrient
status
in
relaLon
to
maize
nutrient
requirement
Ø N
(114
kg
ha-­‐1)
and
P
(17
kg
ha-­‐1)
show
the
‘widest’
gap
for
maize
producLon
Ø Botswana
followed
by
Mozambique
show
large
N
gap
Ø Malawi
and
Zimbabwe
low
(113
kg-­‐ha-­‐1)
N
gap
Ø Botswana
(30
kg
ha-­‐1)
followed
by
Angola
(27
kg
ha-­‐1)
widest
P
gap
Ø Malawi
has
no
P
gap
–
subsidy?
Results:
gaps
in
relaLon
to
maize
nutrient
requirements

15. Approximate
cost
required
to
replenish
the
observed
gap
Depending
on
countries
and
household
status,
farmers
may
be
able
to
pay
for
ferLlizer
but
not
to
fill
the
whole
‘gap’
Results:
Overall
cost
of
input
N
and
P
input
to
replenish
gap
0
50
100
150
200
250
Angola
Botswana
Malawi
Mozambique
Zambia
Zimbabwe
Cost
of
nutrient
(US$
per
ha)
Nitrogen
Phosphorus

16. Considering
the
limited
income
of
smallholders
and
the
vulnerability
of
agriculture
to
different
kinds
of
stresses,
it
maybe
difficult
for
some
farmers
to
fill
the
‘gap’
with
ferLlizer
input.
q AlternaLve
opLons
needed
Households
ordered
by
their
total
annual
income
(USD$)
Total
annual
income
($USD)
Results:
Overall
cost
of
input
N
and
P
input
to
replenish
gap

17. Results:
Overall
cost
of
input
N
and
P
input
to
replenish
gap
Alterna3ves
such
as
organic
inputs,
intercropping,
fer3lizer
crops,
CA
can
help
supplement
inorganic
input
Need
to
‘encourage’
farmers
to
use
those
op3ons!
Supplement
with:
credit,
insurance,
subsidy,
…

18. Conclusion
ü Evidences
show
that
key
nutrients
are
limiLng
in
SSA
ü Input
use
is
low
and
generally
‘blanket’
approach
is
used
ü ‘Site-­‐specific’
informaLon
needed
to
guide
implemenLng
targeted
intervenLon
ü Price
of
ferLlizer
is
high
(8X
where
it
is
sourced
from)
–
encourage
and
enhance
local
producLon,
reduce
tax,
improve
complementary
inputs
+
’green
opLons’
ü Farmers
have
limited
‘economic’
capacity
and
knowledge
to
use
adequate
input
sustainably:
incenLves,
credit
facility
ü Farmers
contemplate
technology
use
–
risk
aversion.
Insurance,
subsidy,
Lmely
informaLon