Climate change adaptation strategies in semi-arid Zimbabwe for sustainable intensification of crop-livestock systems
Climate change adaptation strategies in
semi-arid Zimbabwe for sustainable
intensification of crop-livestock systems
Sabine Homann-Kee Tui, Patricia Masikati,
Katrien Descheemaker, Lieven Claessens,
Olivier Crespo, Andre van Rooyen
1st Int. Conference on Food Security
NH Hotel Leeuwenhorst
29. September – 02. October, 2013
• Increasing populations
• Diminishing p. c. food production
• Dwindling natural resource base
• Climate change
Introduction
Sustainable intensification
of smallholder farming
systems in Southern Africa
Source: Alex Rouane
Median temperature change (oC), Mid-Century, RCP 8.5, S- Africa Median precipitation change (%), Mid-Century, RCP 8.5, S- Africa
Objectives
1. Describe current farming systems and
propose a farming systems typology
2. Assess potential impact of climate
change on future smallholder crop
livestock systems
3. Assess potential economic benefits of
selected climate change adaptations
• Fertilizer applications on maize
• Maize-Mucuna rotation
MethodsClimate data
Historical (1980-2010):
Mid century (2040-2070):
RCP 8.5 (CMIP5)
20 GCMs
Projected changes in
temperature,
precipitation
Crop Model
APSIM
5 GCMs
0kgN/ha (FP)
17kgN/ha
52kgN/ha
Maize-Mucuna rotation
Effects on on-farm maize
and Mucuna production
Livestock model
Livsim (Rufino et al.)
Feed gaps
On-farm feed production
(crop residues, forages)
Effects on livestock
production (milk, off-
take, mortality rates)
Economic model
TOA-MD (Antle et al.)
2GCMs
HH survey data (n=160)
Relative yields
Prices, costs
Regional RAPs (Global and
regional models)
Economic trade-offs of
climate change and
adaptation strategies
on entire farms
Economic impacts
Heterogeneous populations
Types of households
∎ HISTORICAL
A = ACCESS1-0
B = bcc-csm1-1
C = BNU-ESM
D = CanESM2
E = CCSM4
F = CESM1-BGC
G = CSIRO-Mk3-6-0
H = GFDL-ESM2G
I = GFDL-ESM2M
J = HadGEM2-CC
K = HadGEM2-ES
L = inmcm4
M = IPSL-CM5A-LR
N = IPSL-CM5A-MR
O = MIROC5
P = MIROC-ESM
Q = MPI-ESM-LR
R = MPI-ESM-MR
S = MRI-CGCM3
T = NorESM1-M
Climate data
RCP8.5 GCMs for Nkayi in Zimbabwe
historical - mid century
averaged over the growing season
Projected temperature changes
• Strong signal that temperature will increase (by +2 - +3.3oC)
• Temperature increase is all year, esp. during the early growing season
Projected precipitation changes
• No strong signal on precipitation changes (-0.7mm/day - +0.5mm/day)
• Precipitation decreases esp during earlier rainy season
RCP 8.5 mid century temperature scenarios for all GCMs in Nkayi, Zimbabwe
RCP 8.5 mid century precipitation scenarios for all GCMs in Nkayi, Zimbabwe
Effects of CC and technologies
on maize grain and mucuna yields
•Maize grain yields decrease up to
>20%
•Mucuna biomass decrease up to
> 20%, but still provides high feed
biomass
•Fertilizer and Mucuna applications
offset CC effects
•Uncertainty among GCMs
Crop modeling
Effects of CC and technologies
on livestock performance
•Marginal effect of CC on milk
production and mortality rates
•Increased milk production and
reduced mortality under fertilizer
and Mucuna scenarios
•Uncertainty among GCM
Farming systems parameters
Current systems Small farms Medium farms Large farms
Share of HH (n=160) % 43 38 19
Crop production (maize + other crops)
• Cultivated land
• Maize yields
• Net returns
ha/farm (sd)
Kg/ha (sd)
USD/farm (sd)
1.3 (0.7)
497 (444)
90 (85)
1.8 (0.8)
826 (623)
217 (152)
2.5 (1.4)
675 402)
110 (131)
Livestock production (cattle + other livestock)
• Herd size
• Milk yield
• Net returns
TLU/farm (sd)
l/cow/day (sd)
USD/farm (sd)
0.3 (0.4)
0
9 (23)
5.8 (2.5)
0.8 (0.7)
495 (322)
15.5 (4.9)
1.3 (0.8)
1376 (586)
Off-farm income USD/farm (sd) 246 (250) 324 (288) 345 (431)
Selected producer prices (USD/kg) based on RAPs Current Mid term
Maize grain (residues)
Other crops grain (residues)
Mucuna biomass
0.20 (0.04)
0.25 (0.04)
0.17
0.22 (0.04)
0.26 (0.04)
0.18
Beef
Milk
1.3
1
1.43
1.05
Exogenous growth (%) based on RAPs Mid term
Maize
Other crops
30
20
Cattle
Other livestock
10
10
-2000
-1500
-1000
-500
0
500
1000
1500
2000
0 10 20 30 40 50 60 70 80 90 100
Economiclosses(USD/farm)
Percentage of farm population
GCME_no RAPs GCMK_no RAPs
GCME_with RAPs GCMK_with RAPs
Economic modeling
1. Impact of CC on future agricultural production systems
Scenarios: C0F0M0 – C1F0M0 with/without RAPs
• 44 to 56% farms are negatively affected by CC
- Livestock reduces negative effects of CC
• Net losses from CC are marginal (6 to -3%)
• Economic development can offset CC impact
• Uncertainty among GCMs
% gainers
% losers
2. Benefits of CC adaptations
Scenarios: C1F1M0, C1F2M0, C1F0M1 – with RAPs
-2000
-1500
-1000
-500
0
500
1000
1500
2000
0 10 20 30 40 50 60 70 80 90 100
Opportunitycosts(USD/farm)
Percentage of farm population
GCMEF17 GCMEF52 GCMEMuc
GCMKF17 GCMKF52 GCMKMuc
•CC adaptation technologies benefit most farms
•Net effects of fertilizer applications are small
•Maize-Mucuna rotation provides higher benefits, against less risk
% adopters
% non-adopters
3. Impact of CC adaptations by farm types
GCME GCMK
Stratum Small Medium Large Total Small Medium Large Total
Net losses per farm (USD/farm)
CC_no RAPs 11 2 165 37 -5 -35 -23 -20
CC_with RAPs -32 -161 -80 -91 -47 -211 -303 -159
F17 -6 -87 -251 -85 -2 -194 -199 -113
F52 19 -160 -149 -82 22 -158 -136 -77
Mucuna -80 -235 -493 -219 -109 -368 -331 -251
Poverty rate (% of population living < 1 USD/day)
CC_no RAPs 100 98 62 86 100 98 66 87
CC_with RAPs 100 95 59 85 100 93 55 83
F17 100 90 47 79 100 82 43 75
F52 100 85 48 78 100 82 46 76
Mucuna 100 84 40 75 100 74 39 71
• Net effects from CC are marginal for small farms - any positive incentive
will improve their system
• Medium and better off farms benefit more from CC adaptations, but also
face higher risk
• CC adaptation can reduce the proportion of people below poverty rate by
10-15% - but poverty remains high
New routes for sustainable intensification?
– CC impacts on entire farm net benefits are small
in semi-arid Zimbabwe
– Greater impact on farm households’ well being
through
• Economic changes – policy and institutional
interventions
• Alternative income options – opportunities for
reinvestments
– Transition towards CC resilient + profitable
farming systems requires a drastic shift
• From high risk to more diversified systems
(maize-cattle→maize-cattle –mucuna-smallstock)
• Coupling crop livestock systems: livestock as currency
for intensification
• CC adaptation technologies tailored to farm types
ICRISAT is a member of the CGIAR Consortium
Picture here
Thank you!
This work was supported by the
CGIAR Research Program CCAFS.
Data were used from previous
CGIAR-SLP.
Special kuddos to Roberto Valdiva!